Fix JVP rule for lax.pow()

[jakevdp]

Fixes #12033

[jakevdp]

I double-checked against the analog of the code in HIPS/autograd#116, which inspired the introduction of this select statement. I get the same result before and after the change in this PR:

import jax
import jax.numpy as jnp

def fun(x):
    return jnp.exp(-(x*x))
d1 = jax.grad(fun)
d2 = jax.grad(d1)
d3 = jax.grad(d2)
d4 = jax.grad(d3)
print(d1(0.), d2(0.), d3(0.), d4(0.))
# -0.0 -2.0 0.0 12.0

def fun(x):
    return jnp.exp(-(x**2))
d1 = jax.grad(fun)
d2 = jax.grad(d1)
d3 = jax.grad(d2)
d4 = jax.grad(d3)
print(d1(0.), d2(0.), d3(0.), d4(0.))
# -0.0 -2.0 0.0 12.0

[jakevdp]

So, it turns out there is one (untested) case that this PR changes:

jax.grad(jax.lax.pow, 0)(0.0, 0.0)

On main, this returns 0.0; with this PR, it returns NaN.

Chatting offline with @mattjj, we realized this is similar in spirit to the sinc zero issue in (#5054); the resolution there was to do a custom JVP rule using the maclaurin series (#5077), and I think the most rigorously-correct solution here will probably be similar.

But as the current approach returns incorrect results, we'll merge this change before working on the 0**0 corner case, which will take a bit more thought.