First, recall the soluction of the simpler problem without the maximum restriction (the answer for that is $N + jD -1 \choose N$). This can be obtained by noting that for one box there is always only one configuration (put all the balls in the box) which I denote $S_{1, k} = 1$. For two boxes, if you put $m$ balls in the first box, the number of balls in the second one is fixed and so the answer is $N+1$, so this can be regarded as a sum over the number of solutions for one box. In general for $N$ boxes you obtain the answer as an $N$-fold convolution of the constant function $S_{1,k}$. In particular, for all $N$ the answer is a polynomial in $k$.
For your problem, the solution is similar. I'll denote by $T^D_{N, k}$ the number of ways of putting $k$ balls into $N$ boxes with $D - 1$ maximum of balls in one box. Then $T^D_{1, k} = 1_{k < D}$ (i.e. 1 when $k$ is less than $D$). Now, observe that $T^D_{N,k} = S_{N,k}$ when $k < D$ (since in this range the maximum condition does not apply). Therefore, the $T^D_{N,k}$ for $N$ fixed looks again like a polynomial of degree $N-1$. But at $k=jD$ there is a discontinuity in the $(N-1)^{st}$ derivative corresponding to the maximum being hit. The graph looks like this for $D=6$:

So, what actually happens here is that we are summing the i.i.d random variables with uniform distribution. As a result, for $N \to \infty$ this approaches a normal distribution by the central limit theorem.