Note that the quantity that you want to compute is
$$
\newcommand\ket[1]{\left|#1\right\rangle}\newcommand\bra[1]{\left\langle#1\right|}
\begin{align}
\mathbb{E}\left[\ket{0^{n-1}}\!\bra{0^{n-1}}\otimes\ket{0^{n-1}}\!\bra{0^{n-1}}\rho\otimes\rho\right]&=\mathbb{E}\left[\bra{0^{n-1},0^{n-1}}\rho\otimes\rho\ket{0^{n-1},0^{n-1}}\right]\\
&= \mathbb{E}\left[\left(\bra{0^{n-1}}\rho\ket{0^{n-1}}\right)^2\right]\,.
\end{align}
$$
Let us denote $\ket{\psi}$ the Haar-random state that is sampled on $n$ qubits. For an index $i=\left(i_1,\cdots,i_n\right)\in\{0, 1\}^n$, we note $\psi_i$ the coefficient of $\ket{i_1,\cdots,i_n}$ in $\ket{\psi}$. We then have
$$\begin{align}
\ket{\psi}\!\bra{\psi} ={}& \sum_{i,j\in\{0, 1\}^n}\psi_i\overline{\psi_j}\ket{i}\!\bra{j}\\
={}&\sum_{i,j\in\{0, 1\}^{n-1}}\psi_{i,0}\overline{\psi_{j,0}}\ket{i,0}\!\bra{j,0}+{}&\\
&\sum_{i,j\in\{0, 1\}^{n-1}}\psi_{i,0}\overline{\psi_{j,1}}\ket{i,0}\!\bra{j,1}+{}\\
&\sum_{i,j\in\{0, 1\}^{n-1}}\psi_{i,1}\overline{\psi_{j,0}}\ket{i,1}\!\bra{j,0}+{}\\
&\sum_{i,j\in\{0, 1\}^{n-1}}\psi_{i,1}\overline{\psi_{j,1}}\ket{i,1}\!\bra{j,1}\,.
\end{align}$$
Tracing over the last qubit (of course, the reasoning is the same for any qubit) yields
$$\rho = \sum_{i,j\in\{0, 1\}^{n-1}}\psi_{i,0}\overline{\psi_{j,0}}\ket{i}\!\bra{j}+\sum_{i,j\in\{0, 1\}^{n-1}}\psi_{i,1}\overline{\psi_{j,1}}\ket{i}\!\bra{j}\,.$$
We thus have
$$\bra{0^{n-1}}\rho\ket{0^{n-1}}=\left|\psi_{0^{n}}\right|^2+\left|\psi_{0^{n-1}, 1}\right|^2\,.$$
Since I'm still not up-to-date on Weingarten calculus despite how useful it is, we'll do it the old way: writing $\psi_i$ as $\frac{X_i+\mathrm{i}Y_i}{\sqrt{\sum_jX_j^2+Y_j^2}}$, with the $\left\{X_j\right\}_j$ and the $\left\{Y_j\right\}_j$ being i.i.d. according to a $\mathcal{N}(0, 1)$ law. In particular, we have
$$\bra{0^{n-1}}\rho\ket{0^{n-1}}=\frac{X_{0^n}^2+Y_{0^n}^2+X_{0^{n-1}, 1}^2+Y_{0^{n-1}, 1}^2}{\sum_jX_j^2+Y_j^2}\,.$$
In particular, we can write this quantity as $\frac{A}{A+B}$, with $A\sim\chi^2(4)=\Gamma\left(2,2\right)$ and $B\sim\chi^2\left(2^{n+1}-4\right)=\Gamma\left(2^n-2,2\right)$ being independent. As such it follows a $\beta\left(2, 2^n-2\right)$ law.
Now that we have the distribution of the random variable, we know everything we want to know about it. In particular, its second moment is
$$\frac{6}{2^n\left(2^n+1\right)}=\boxed{\frac{3}{2^{n-1}\left(2^n+1\right)}}\,.$$