Let $\left(B_t\right)_{t\in[0,1]}$ be a family of independent random variables with $B_t$ being a Bernoulli random variable of parameter $t$ for every $t\in[0,1]$. Write $h(t)$ for the entropy (in the natural base $\text{e}$) $H\left(B_t\right)$ for all $t\in[0,1]$. Observe that $h:[0,1]\to\mathbb{R}_{\geq 0}$ is given by
$$h(t)=-t\,\ln(t)-(1-t)\,\ln(1-t)$$
for all $t\in(0,1)$, and $h(0)=h(1)=0$.
Now, fix $x,y\in[0,1]$. First, note that
$$H\left(B_xB_y\big|B_x\right)=x\cdot H\left(B_y\right)+(1-x)\cdot H\left(B_0\right)=x\cdot h(y)+(1-x)\cdot h(0)=x\cdot h(y)\,.$$
Thus,
$$H\left(B_xB_y,B_x\right)=H\left(B_xB_y\big|B_x\right)+H\left(B_x\right)=x\cdot h(y)+h(x)\,.$$
On the other hand,
$$H\left(B_xB_y,B_x\right)=H\left(B_x\big|B_xB_y\right)+H\left(B_xB_y\right)\geq H\left(B_xB_y\right)=H\left(B_{xy}\right)=h(xy)\,.$$
Consequently,
$$h(xy)\leq x\cdot h(y)+h(x)\leq h(x)+h(y)\,.$$
The inequality $$h(xy)\leq h(x)+h(y)$$ for all $x,y\in[0,1]$ becomes an equality if and only if $x=1$, $y=1$, or $(x,y)=(0,0)$. Observe that $f_1=-\big(h(x)+h(y)\big)$ and $f_2=-h(xy)$, up to a positive scalar multiple (if you use a different logarithmic base $b>1$). Therefore, $f_1\leq f_2$.