Let $g \sim N(0, (1/d)I_d)$ be independent of $x$. Then $g_1 \overset{\mathcal L}{=} \|g\| x_1$, so $(x_1, \|g\|x_1)$ is a coupling between the marginal distribution of $x_1$ and $N(0, 1/d)$.

The norm $\|g\|$ is sharply concentrated around $1$, with fluctuations of order $1/\sqrt{d}$, so the Wasserstein distance is bounded above by
$$\mathbb E|x_1 - \|g\|x_1| = (\mathbb E|1 - \|g\||) \mathbb E|x_1| = O(d^{-1}),
$$
where we have used the fact that $\mathbb E|x_1|^2 = (1/d)\mathbb E\|x\|^2 = 1/d$ by isotropy of the distribution of $x$, so that $\mathbb E|x_1| \le \sqrt{\mathbb E|x_1|^2} = 1/\sqrt{d}$ thanks to Jensen's inequality.