If $x$ is a fixed unit vector and $a \sim \mathcal{N}(0, I)$, then
$$\mathbb{E}\left[ (a^{\top}x)^{2}aa^{\top}\right] = 2xx^{\top} + I$$
This can be intuitively verified when $x$ is a unit vector corresponding to coordinate axes $x = \mathrm{e}_i$ by using fourth moment of gaussian $\mathbb{E}\left[a_i^4\right] = 3$ and pairwise independence between the components $a_i$ of $a$.
However I am not able to complete the proof of above moment calculation.
\begin{align*} \mathbb{E}\left[ (a^{\top}x)^{2}aa^{\top}\right] &= \mathbb{E}\left[ a^{\top}xx^{\top}aaa^{\top}\right] \\ &= \mathbb{E}\left[ x^{\top}aa^{\top}xaa^{\top}\right] \\ &= \mathbb{E}\left[ x^{\top}\left(aa^{\top}-I\right)xaa^{\top}\right] + I \\ \end{align*}
I couldn't find a nice trick, but computing each entry directly seems to work.
The $(i,j)$ entry of $aa^\top$ is $a_i a_j$, so the $(i,j)$ entry of $(a^\top x)^2 aa^\top$ is $a_i a_j \left(\sum_k a_k x_k\right)^2 = a_i a_j \sum_{k,l} a_k a_l x_k x_l$ .
So, the expectation of the whole matrix is $2xx^\top + I$.