it is possible to show that $\mathrm {E}d(P,\hat{P_n})\sim \frac{C}{\sqrt{n}}$ and specify the value of $C$.

let

$$D_n^2 =\sum_{x \in \mathcal{X}} \left( \sqrt{P(x)} - \sqrt{\hat{P_n}(x)} \right)^2 = 2d^2(P,\hat{P_n}). $$

$4nD_n^2$ is known in statistics [for reasons unclear to me] as the freeman-tukey goodness-of-fit [gof] statistic for testing the null hypothesis that $X\sim P$. like the better known pearson chi-squared gof statistic, it also has [under the null hypothesis] an asymptotic chi-squared distribution with $k-1$ df. here $k=|\mathcal{X}|$.

the statistic $D_n^2$ seems to have been first considered by Matusita in *On the Estimation by the Minimum Distance Method*. in *Decision Rules, Based on the Distance, for Problems of Fit, Two Samples, and Estimation*, Matusita develops some asymptotic [and other] properties of $D_n^2$, including the fact that under the null hypothesis, as $n\to\infty$,

$$\kern-1.9in (1)\kern1.9in 4nD_n^2\ \buildrel{\mathcal L}\over{\to}\ \chi^2_{k-1}.$$

it is also shown there that

$$\kern-.88in (2)\kern.88in 4nD_n^2\ \le\ \mathbb{X}^2_n\ :=\ n\sum_{x \in \mathcal{X}} \frac{\left({\hat P}(x)-P(x)\right)^2}{P(x)}. $$

$\mathbb{X}^2_n$ is, of course, the pearson chi-squared gof statistic, and it is well-known that under the null hypothesis $X\sim P$, as $n\to\infty$,

$$\kern-2in (3)\kern2in \mathbb{X}^2_n\ \buildrel{\mathcal L}\over{\to}\ \chi^2_{k-1}.$$

it is also easily seen that for all $n\ge 1,\ \mathrm {E} \mathbb{X}^2_n\ =\ k-1$.
together with (3) [and non-negativity], this entails that $\mathbb{X}^2_n$ is uniformly integrable. in view of (2), so is $4nD_n^2$, so it follows from (1) that

$$\mathrm {E}4nD_n^2\to \mathrm {E}\chi^2_{k-1}\ =\ k-1\ \mathrm{as}\ n\to\infty$$

and

$$\mathrm {E}2\sqrt{n}D_n\to \mathrm {E}\chi_{k-1}\ \mathrm{as}\ n\to\infty.$$

[for more details on connections between convergence in law, uniform integrability and convergence of expectations, see *Billingsley 1st ed*, p32 theorem 5.4 or *Billingsley 2nd ed* pp31-32 theorems 3.4 and 3.5.]