This is a consequence of what was discussed in your previous question, that any real-valued function like $\sin(\omega t)$ consists of both positive and negative frequencies.
Multiplying by a complex exponential $e^{i\omega t}$ simply shifts frequency by $\omega$. There are many ways to show this, one way is to simply look it up in a table of Fourier transforms, where you will find the Fourier transform of
$$ f(x) e^{iax} $$
is
$$ \hat f(\omega -a) $$
But multiplying a function by $\sin(ax)$:
$$ f(x) \sin(ax) $$
is
$$ \frac {{\hat {f}}(\omega -a)-{\hat {f}}(\omega +a)}{2i} $$
This equation is the basis of a frequency mixer which produces new signals at the sum and difference frequencies.
In your example with complex-valued blocks, you begin with a complex exponential which has a signal at just one frequency. Then you multiply that by another complex exponential, leaving you with still just one frequency, but shifted.
In your example with real-valued blocks, your first sinusoid begins with signals at two frequencies. Again, any real-valued function must have both positive and negative frequencies. When you multiply that by another sinusoid, you are both shifting those two frequencies you started with up (with the positive frequency) and down (with the negative frequency) present in your second signal block. The end result is the two frequencies are split into four.
This complication of creating sum and difference frequencies is a design challenge in superheterodyne receivers. Since a typical superhet receiver operates on a function represented by a voltage which must be real, every time the receiver employs a mixer to shift frequency up or down there are two frequencies in the input which map to the target output frequency. Therefore, filtering must be employed to ensure there are no signals at this image frequency which would otherwise create interference.
This can be avoided when signals can be complex. In analog designs it's difficult to do this accurately, but digitally it's no problem, and this is part of why much digital signal processing is done with complex values, rather than real values.