Rewrite scalar dot as multiplication

Description

In https://github.com/pymc-devs/pytensor/pull/1178 we rewrite batched dots that are just multiplication away, but left core dots the same due to use of BLAS operations for those (whether they are worth it or not is a question on its own). But there is one case that is definitely not worth it: scalar multiplication.

The following graph should definitely be simplified:

import pytensor
import pytensor.tensor as pt

x = pt.tensor("x", shape=(1, 1))
y = pt.tensor("y", shape=(1, 1))
out = x @ y
pytensor.function([x, y], out).dprint()

CGer{non-destructive} [id A] 2
 ├─ [[0.]] [id B]
 ├─ 1.0 [id C]
 ├─ DropDims{axis=1} [id D] 1
 │  └─ x [id E]
 └─ DropDims{axis=0} [id F] 0
    └─ y [id G]

Or without BLAS stuff

pytensor.function([x, y], out, mode="FAST_COMPILE").dprint()

Dot22 [id A] 0
 ├─ x [id B]
 └─ y [id C]

Those should just be mul because that can be fused with other Elemwise operations (and calling BLAS for it is the silliest thing ever)

Reproduce the issue with the provided pytensor.tensor examples and inspect the optimizer path used by pytensor.function, including FAST_COMPILE and dprint output. The change is done when scalar matrix dots are represented as mul rather than Dot22 or CGer, while preserving the shown computation.