It's a rather elementary fact that a sum of two independent normally distributed random variables $X$ and $Y$ is normally distributed (or, if you will, the convolution of two normal densities is a normal density). To what extent does this go the other way around? It seems that is $X$ and $Y$ are independent and $X$ is normally distributed, then $Y$ is normally distributed or constant.
If you drop both independence of $X$ and $Y$ and $X$ being normally distributed, it's pretty easy to e.g. take $X \sim \mathcal{N}(0,1)$, $Y = 1(X \geq 0)$ and consider $U=XY$, $V=X \,1(Y = 0)$. Now neither $U$ or $V$ are normally distributed but $U+V = X$.
However, if you require $X$ and $Y$ to be independent, but drop the requirement of $X$ being normally distributed, it's seems more difficult. Is there a counterexample in that case, where $X$ and $Y$ are not normally distributed but $X+Y$ is?