First of all apologies that I am a novice statistician.
My data set is a national panel survey from 2013-2019
I am trying to determine if a pension reform in 2016 results in lower working hours
reform targets 20-30hr work week, firm size 500+ workers

ln_hr is the outcome variable log of weekly hrs

the did variables are the treatment group x treatment year:

did = actual treatment group x treatment year

In addition I added the did effect for 2 closely related groups that were not affected
did2= firm size below 500 x treated year
did3= over 30hr work week x treated year

age groups: 1 (young), 2 (prime), 3 (old)
sex=1 male,
married=0 (unmarried), 1(married)

The thing is, I'm confused on how I should interpret my regression results

I've run this first on prime males then I try with female and other age groups:
Code:
. xtreg ln_hrs did did2 did3 married age age_sq if sex==1 & agegroup==2,
note: married omitted because of collinearity

Fixed-effects (within) regression               Number of obs     =  1,413,097
Group variable: pid2                            Number of groups  =    506,653

R-sq:                                           Obs per group:
within  = 0.0484                                         min =          1
between = 0.0126                                         avg =        2.8
overall = 0.0144                                         max =          8

F(5,906439)       =    9227.91
corr(u_i, Xb)  = -0.3694                        Prob > F          =     0.0000


ln_hrs       Coef.   Std. Err.      t    P>t     [95% Conf. Interval]

did   -.3918229   .0031649  -123.80   0.000    -.3980259   -.3856199
did2   -.2259312   .0024079   -93.83   0.000    -.2306506   -.2212118
did3    .1183826   .0010739   110.24   0.000     .1162778    .1204873
married           0  (omitted)
age   -.0053248   .0019617    -2.71   0.007    -.0091697   -.0014799
age_sq   -.0000975   .0000219    -4.44   0.000    -.0001405   -.0000545
_cons     4.18519   .0436677    95.84   0.000     4.099603    4.270778

sigma_u   .30998708
sigma_e   .20286125
rho   .70015095   (fraction of variance due to u_i)

F test that all u_i=0: F(506652, 906439) = 4.51              Prob > F = 0.0000

Code:
. estimates table male_prime    male_old male_young female_old female_young, stats(N    r2
        
        
Variable  male_prime    male_old    male_young  female_old   female_y~g      
        
did   -.3918229    .20869769    .14312667    .02250853    .16162787      
did2  -.22593119    -.0212681    .18365077    .13229586     .18334501      
did3   .11838256    .23876996    .29431827    .28468336    .32835838      
married   (omitted)    (omitted)    (omitted)    (omitted)    (omitted)      
age  -.00532479    .08906627   -.00525672    .00457667    .48950869      
age_sq  -.00009747    .00039557    .00070005   -.00021192   -.01037898      
_cons   4.1851905    7.5631666    3.1662467    3.5781754   -2.4026562      
        
N     1413097    198487       116533      1136866      113062      
r2   .04843646    .05134639    .04625304    .04583826    .05474549      
r2_a  -.48344304    .53456717   -.71746081   -.51141495   -.69389812
My first question is how I am supposed to interpret each of the did coefficients?
The null hypothesis is that there is no difference between the experimental and control groups and I don't know if I can reject it here.