Allow lists as inputs to variables and equations in `pytensor.optimize.root`

Description

Currently the following program is not allowed:

a, b, c, d, e, f, x, y = pt.dscalars('a b c d e f x y'.split())

eq_1 = a * x ** 2 - b * y - c
eq_2 = d * x - e * y ** 2 + f

solution,  success = root(equations=[eq_1, eq_2], variables=[x, y],
                        method='hybr',
                        optimizer_kwargs={'tol':1e-8})

This has an easy work-around:

a, b, c, d, e, f = pt.dscalars('a b c d e f'.split())
xy = pt.dvector('xy', shape=(2,))

equations = pt.stack([a * xy[0] ** 2 - b * xy[1] - c, d * xy[0] - e * xy[1] ** 2 + f])

solution,  success = root(equations=equations, variables=xy,
                        method='hybr',
                        optimizer_kwargs={'tol':1e-8})

But I think it's a bit of a bummer the first one doesn't work. First, the function argument is plural, so it seems to suggest that it could take a list. Second, we're making users do more work that we could probably figure out for them.

If we allow list inputs, I have 2 ideas:

1. We could add special logic to handle inner functions with multiple outputs, and do a bunch of concatenation to get a single root vector and a single jacobian matrix (the latter would be similar to how we handle the args)
2. We could do some graph rewriting to get everything into a canonical form, like what we do with the scalar case? Make a single input_variable_vector, then graph_replace equations to take that thing as input?

Case (2) seems better, but curious if it's too complex.

As a side note, this is something I've run into often when working with systems of equations, so some general helpers might be called for.

Start at the pytensor.optimize.root entry point and compare the existing scalar case with its handling of args. Inspect the proposed graph_replace approach and input_variable_vector idea, then determine how list equations and variables should be normalized. Done means the list-based example works without manual stacking and the behavior is covered by regression tests.