EDIT: I'm not sure if this is the kind of thing you are looking for, but there is a sequence of SDP relaxations which will give you upper bounds on the objective function. To construct these, we will need the completely positive matrices, which form a convex cone dual to the copositive matrices above. The completely positive matrices are convex combinations of outer products $xx^T$ where $x\geq 0$.
Your problem can be rewritten as maximizing $Tr(AX)$ subject to the conditions that $Tr(X) = 1$ and $X = xx^T$ for $x\geq 0$. Since the objective is now linear, we can go ahead and convexify the feasible set to get the equivalent problem of maximizing $Tr(AX)$ subject to $Tr(X)=1$ and $X$ is completely positive.
Now, being dual to copositivity, complete positivity is also hard to test. But there are a nice sequence of SDP relaxations which give tighter and tighter outer approximations to the completely positive cone; these are just dual to the inner approximations of the SDP cone given by Parrilo ("Semidefinite Programming Based Tests for Matrix Copositivity"). For example, the first relaxation is that $X$ be elementwise nonnegative and PSD, two obvious necessary conditions for complete positivity (in fact these are sufficient for $4\times 4$ and smaller matrices, in which case the relaxation is exact).
Substituting in any such relaxation will give you an SDP which upper bounds the value of your problem of interest.