The date of marriage could be considered analogous to the date of diagnosis of a condition in a clinical study, taken as the date of entry into the study. Time t=0 for each case would be the date of marriage (or diagnosis) and the time to event or censoring would be expressed relative to that date. There is no general need to take that date into account unless the actual date of entry into the study is also associated with the risk of the event of interest (divorce, in your case).
You, however, are already postulating that the actual date of entry into the study matters: you are taking the "year of reform" as being a divider that separates your cases into what could be considered 2 treatment group. So readers will certainly want to see evidence that something special happened in that year that couldn't be explained as some secular trend in divorce rates independent of the "reform." For example, if divorce rates were always increasing with time regardless of the "reform," then comparing marriages before and after the date of "reform" would still show higher divorce rates thereafter.
So in your case careful modeling of the relationship of divorce rate to actual year of marriage would seem very important.
One more thought: if your time values are measured in long intervals like years instead of days, then you might want to consider a discrete-time survival model instead.