`local_add_neg_to_sub` rewrite gives wrong results with negative constants

[ricardoV94]

Reported in https://discourse.pymc.io/t/shape-issue-with-custom-logp-in-densitydist/13608/5?u=ricardov94

import numpy as np

import pytensor
import pytensor.tensor as pt

x = pt.scalar("x")
neg_const = pt.as_tensor(np.full((10,), -0.1))
out = neg_const + x 

pytensor.function([x], out)

<<!! BUG IN FGRAPH.REPLACE OR A LISTENER !!>> <class 'TypeError'> The type of the replacement (Vector(float64, shape=(1,))) must be compatible with the type of the original Variable (Vector(float64, shape=(10,))). local_add_neg_to_sub
ERROR (pytensor.graph.rewriting.basic): Rewrite failure due to: local_add_neg_to_sub
ERROR (pytensor.graph.rewriting.basic): node: Add([-0.1 -0.1 ... -0.1 -0.1], ExpandDims{axis=0}.0)
ERROR (pytensor.graph.rewriting.basic): TRACEBACK:
ERROR (pytensor.graph.rewriting.basic): Traceback (most recent call last):
  File "/home/ricardo/Documents/Projects/pytensor/pytensor/graph/rewriting/basic.py", line 1968, in process_node
    fgraph.replace_all_validate_remove(  # type: ignore
  File "/home/ricardo/Documents/Projects/pytensor/pytensor/graph/features.py", line 626, in replace_all_validate_remove
    chk = fgraph.replace_all_validate(replacements, reason=reason, **kwargs)
  File "/home/ricardo/Documents/Projects/pytensor/pytensor/graph/features.py", line 571, in replace_all_validate
    fgraph.replace(r, new_r, reason=reason, verbose=False, **kwargs)
  File "/home/ricardo/Documents/Projects/pytensor/pytensor/graph/fg.py", line 508, in replace
    self.change_node_input(
  File "/home/ricardo/Documents/Projects/pytensor/pytensor/graph/fg.py", line 419, in change_node_input
    raise TypeError(
TypeError: The type of the replacement (Vector(float64, shape=(1,))) must be compatible with the type of the original Variable (Vector(float64, shape=(10,))).

https://github.com/pymc-devs/pytensor/blob/c52154d3116daa344e1abc53253d969b9fd0c181/pytensor/tensor/rewriting/math.py#L1897-L1922

The helper get_constant actually calls get_unique_constant_value under the hood, which in this case returns a single scalar, not the filled homogenous vector. The rewrite itself is also expecting only a scalar constant, otherwise it should be using np.all