is there a difference between E[e|x]=0 and E[e|d=1]-E[e|d=0] in continuous vs discrete case in regressions?
It is very unclear what you are asking. But in most regressions it is assumed that the error term (I assume "e" is the error term) is assumed to be iid for all values of x. In particular, in the discrete case $E[\epsilon|d=1]=E[\epsilon|d=0]$, but this can be generalized by saying $E[\epsilon|x]=E[\epsilon|x']$ for all $x, x'$. Now if your regression also includes a constant term, it is without loss of generality to assume that the expected value of $\epsilon$ (which is the same for all values of x) is equal to zero. Thus, the importance of the assumption $E[\epsilon|x]=0$ is not so much the "equal to zero" part, but the fact that is the same for all values of x.