Implement IfElse with inner functions so it works in all linkers

Description

IfElse is only lazy in the default backend because the function virtual machine handles it (via the "lazy" attribute"). In Numba/JAX it currently does nothing, because it receives all outputs pre-computed.

During compilation we could specialize IfElse into a LazyIfElse Op that contains two inner Graphs, one corresponding to each branch. These graphs should contain all variables that lead to the inputs of IfElse and are not used by any other output variable other than through the outputs of IfElse. This depends on which function is being compiled and can't be known ahead of time.

The current implementation of jax_funcify_IfElse:
https://github.com/pymc-devs/pytensor/blob/4235ccc3f4243c5179178a206c15d84c4cda2e79/pytensor/link/jax/dispatch/basic.py#L60-L70

Would instead look something like (pseudo-code):

@jax_funcify.register(LazyIfElse)
def jax_funcify_LazyIfElse(op, **kwargs):
    true_fn = jax_funcify(op.true_fgraph)
    false_fn = jax_funcify(op.false_fgraph)

    def ifelse(cond, *args):
        res = jax.lax.cond(cond, true_fn, false_fn, *args)
        return res if n_outs > 1 else res[0]

    return ifelse

This could even provide a nicer dprint, by showing the two inner graphs. Right now it's not always obvious what operations are lazily computed or not.

Start with the current jax_funcify_IfElse implementation in pytensor/link/jax/dispatch/basic.py and trace how IfElse is compiled for each linker. Determine how compilation can specialize it into a LazyIfElse with true and false inner Graphs containing only branch-specific inputs. Done means lazy evaluation works across linkers and the dprint exposes the two inner graphs.