Expected entropy in $P(x)\oplus x$ for random $x$, where $P$ is a random permutation
All functions (including permutations) mapping a finite field $\mathbb{F}_q$ to itself can be uniquely represented by polynomials (via Lagrange Interpolation, possibly followed by reduction modulo $x^{q}-x.$ When $q=2^n,$ the linear permutations $F(x)=x+a,$ for $a\neq 0,$are exactly the type of polynomial that give zero entropy in your formulation since $F(x)+x=a$ a constant. 

Focusing only on $q=2^n$ due to crypto applications, we don't know explicit representation of many other permutations but some include certain monomials $F(x)=x^{e}$ with special conditions on exponent $e,$ as well as families such as Dickson's polynomials, which don't have an $x$ term in their polynomial and won't suffer the weakness of zero entropy for $F(x)+x$. As for computing what fraction of overall permutation polynomials have an $x$ in their polynomial expression, it's likely to be a difficult problem.

Edit: The below is redundant, due to your comment, the above is still relevant but I can take it to a comment if requested, it's a bit long.

If we model the map $F(x)=P(x)+x$ by a random mapping instead of a random permutation from $A=\{0,1\}^n$ to itself, then the number of points $y$ in $A$ that have no preimages, i.e., there is no $x\in A$ with $f(x)=y,$ is on average equal to $e^{-1}\#A=\frac{2^n}{e}.$

This would mean that the expected size of the range of $F$ is $$\mathbb{E}[F(A)]=(1-e^{-1}) 2^n\approx 0.6321 \cdot 2^n$$ yielding $$H(n)\approx n+\log_2(0.6321)= n-0.66172,$$
so as you predicted the  $\lim_{n\rightarrow \infty}H(n)/n=1.$

References for this type of argument include Sedgewick and Flajolet's book An Introduction to the Analysis of Algorithms, and the paper "Random Mapping Statistics" by Flajolet and Odlyzko in Eurocrypt'89, but a very readable account is in Chapter 5 of Riordan's classic book An Introduction to Combinatorial Analysis, from 1958.