BUG: `np.ma.innerproduct` throws error with object arrays

Describe the issue:

On the behavior observed in the examples below, np.ma.innerproduct appears to mishandle object-dtype arrays. For the same inputs, np.inner returns the a scalar result, while np.ma.innerproduct raises an internal AttributeError.

Similar to #31019, I report it separately from #31735 because it affects a different API and fails with an exception.

Reproduce the code example:

import numpy as np
a = np.array([1,2,3], dtype=object)
b = np.array([0,1,0], dtype=object)

np.inner(a, b)           # -> 2
np.ma.innerproduct(a, b) # -> AttributeError

Error message:

AttributeError: 'int' object has no attribute 'view'

Python and NumPy Versions:

2.6.0.dev0+git20260623.4357ad1
3.12.3 (main, Mar 23 2026, 19:04:32) [GCC 13.3.0]

Runtime Environment:

No response

How does this issue affect you or how did you find it:

This issue was identified by an automated testing tool currently being developed by our research team.
Before reporting it, we reviewed the code, documentation and checked the latest development version of Numpy to confirm this observation.

Start by running the reported object-array example and compare np.inner with np.ma.innerproduct. Trace the masked-array implementation behind np.ma.innerproduct to find where the scalar is treated as requiring .view; done means the example returns the expected scalar without AttributeError and the behavior has regression coverage.