Fix type promotion consistency for the array API elementwise functions and operators

NumPy's type promotion behavior deviates from the spec, which says that type
promotion should work independently of shapes or values, in cases where one
array is 0-d and the other is not. A helper function is added that works
around this issue by adding a dimension to the 0-d array before passing it to
the NumPy function. This function is used in elementwise functions and
operators. It may still need to be applied to other functions in the
namespace.

Additionally, this fixes:

- The shift operators (<< and >>) should always return the same dtype as the
  first argument.
- NumPy's __pow__ does not type promote the two arguments, so we use the array
  API pow() in ndarray.__pow__, which does.
- The internal _promote_scalar helper function was changed to return an array
  API ndarray object, as this is simpler with the inclusion of the new
  _normalize_two_args helper in the operators.

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