$\newcommand{\C}{\mathbb C}$$\newcommand{\R}{\mathbb R}$A unitary transformationAny linear isometry of $\C^d$ is an isometrya unitary transformation. Therefore, the distribution of the random vector $V:=(X_1,Y_1,\dots,X_d,Y_d)$ is uniform on the unit sphere in $\R^{2d}$, where $X_j:=\Re U_{1,j}$ and $Y_j:=\Im U_{1,j}$. So, $V$ equals $W:=(W_1,\dots,W_{2d})$ in distribution, where $$W_j:=\frac{Z_j}{\sqrt{Z_1^2+\dots+Z_{2d}^2}} $$ and $Z_1,\dots,Z_{2d}$ are iid standard normal random variables (r.v.'s). So, the random vector $(|U_{1,1}|^2 ,\dots,|U_{1,d}|^2)$ equals $R:=(R_1,\dots,R_d)$ in distribution, where $$R_j:=\frac{T_j}{T_1+\dots+T_d} $$ and $T_k:=Z_{2k-1}^2+Z_{2k}^2$, so that $T_1,\dots,T_d$ are iid standard exponential r.v.'s. So, $\max_j|U_{1,j}|^2$ equals $\max_j R_j$ in distribution. The distribution and, in particular, the expectation of $\max_j R_j$ were found a long time ago; see e.g. Irwin (1955) and historical references therein going back to as far as 1897. In particular, according to formula (15) in Cochran's paper, $$E\max_j|U_{1,j}|^2=E\max_j R_j=\frac1d\,\Big(1+\frac12+\dots+\frac1d\Big). $$
