Firstly recall the valid statement:
If $F_1$ and $F_2$ are some distributions with characteristic functions $g(t)$ and $h(t)$, then $F=\alpha F_1+(1-\alpha)F_2$ is their mixture with characteristic function
\begin{align}
f(t)=\alpha g(t)+(1-\alpha) h(t).
\end{align}
Take $\alpha=0{,}5$ for simplicity. Then the equality $F=\alpha F_1+(1-\alpha)F_2$ turns to $2F=F_1+F_2$.
If conversely we take some distributions $F$ and $F_1$, the difference $F_2=2F-F_1$ is not obliged to be some distribution. Say, we can take distribution $F$ degenerate at $1$, and $F_1$ degenerate at $0$, and the difference $F_2=2F-F_1$ is not a distribution at all.
Return to characteristic functions. CF for $F$ equals $f(t)=e^{it}$, CF for $F_1$ is $g(t)=1$. The second summand $h(t)$ in the equality
$$e^{it}=\frac12 \cdot 1 + \frac12 \cdot h(t)$$
is $h(t)=2e^{it}-1$. This is not CF since the absolute value of this function can be greater than $1$.