Softmax fails with integer dtypes only at runtime

Description

Brought up in #846

import pytensor
import pytensor.tensor as pt

x = pt.vector("x", dtype="int64")
out = pt.special.softmax(x)

# Doesn't seem right
out.dprint(print_type=True)
# Softmax{axis=None} [id A] <Vector(int64, shape=(?,))>
# └─ x [id B] <Vector(int64, shape=(?,))>

# No complaints
fn = pytensor.function([x], out)

fn([1, 2, 3])  # TypeError: not a float

We should either raise at graph definition time, or cast the input to float. Scipy is happy to take integers (and return floats), so we could try to do the same.

Start at pt.special.softmax and reproduce the integer-input example through graph construction and function execution. Decide whether integer inputs should be rejected when the graph is defined or produce floating-point outputs, then add regression coverage demonstrating the chosen behavior.