Clarifying interpretation of marginal effects after reghdfe

Hello, I am looking for clarification on post-estimation margins command after reghdfe. I am running a pretty standard Diff-in-Diff (effect of a state-level insurance policy change on cancer screening rates in younger vs older individuals) using reghdfe, and want to confirm that I am using/interpreting the margins command correctly.

Code:

reghdfe screen i.age##i.post i.RACE i.EDUC , absorb(state_num year month) vce(cluster state_num)
(MWFE estimator converged in 4 iterations)
note: 1bn.post is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)

HDFE Linear regression                            Number of obs   =  2,056,819
Absorbing 3 HDFE groups                           F(  10,     14) =     776.20
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0049
                                                  Adj R-squared   =     0.0049
                                                  Within R-sq.    =     0.0042
Number of clusters (state_num) =         15       Root MSE        =     0.2780

                                     (Std. Err. adjusted for 15 clusters in state_num)
--------------------------------------------------------------------------------------
                     |               Robust
              screen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
                 age |
              older  |  -.0260963   .0022391   -11.65   0.000    -.0308987   -.0212939
              1.post |          0  (omitted)
                     |
            age#post |
            older#1  |   .0032939     .00066     4.99   0.000     .0018784    .0047094
                     |
                RACE |
          Black, NH  |   .0078715   .0007183    10.96   0.000     .0063309    .0094121
           Hispanic  |   .0222411    .001537    14.47   0.000     .0189446    .0255376
              Asian  |    .043212     .00239    18.08   0.000     .0380859    .0483381
                     |
                     |
                EDUC |
                 HS  |  -.0067054   .0008325    -8.05   0.000    -.0084909     -.00492
       SOME COLLEGE  |  -.0154122   .0009276   -16.61   0.000    -.0174018   -.0134226
  BACHELOR/GRADUATE  |  -.0364558   .0016695   -21.84   0.000    -.0400365   -.0328751
                     |
               _cons |   .0947413   .0005795   163.48   0.000     .0934983    .0959843
--------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
   state_num |        15          15           0    *|
        year |         9           1           8     |
       month |        12           1          11     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

When I run the margins command (albeit with the 'noestimcheck' option, but I think is kosher for calculating marginal effects), it omits one category of individual. This because it is collinear with the fixed effects, yes?

Code:

. margins age, dydx(post) noestimcheck

Conditional marginal effects                    Number of obs     =  2,056,819
Model VCE    : Robust
--------------------------------------------------------------------------------
               |            Delta-method
               |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
       1.post  |
           age |
        older  |          0  (omitted)
      younger  |   .0032939     .00066     4.99   0.000     .0020004    .0045875
--------------------------------------------------------------------------------

When I do it a different way, it tells me that the predicted change for the older group is 0. This "0" predicted change is garbage, similarly because it is collinear with the fixed effects, yes?

Code:

. margins age#post, noestimcheck

Predictive margins                              Number of obs     =  2,056,819
Model VCE    : Robust

Expression   : Linear prediction, predict()
--------------------------------------------------------------------------------------
                     |            Delta-method
                     |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
            age#post |
            older#0  |   .0945361    .000978    96.66   0.000     .0926192     .096453
            older#1  |   .0945361    .000978    96.66   0.000     .0926192     .096453
          younger#0  |   .0684398   .0012712    53.84   0.000     .0659483    .0709314
          younger#1  |   .0717337   .0017257    41.57   0.000     .0683514    .0751161
--------------------------------------------------------------------------------------

So, do I understand correctly that there is no way to get the marginal effect of pre vs post for the older group? Thanks