deduplicating arrays in columns with mixed data types (Python)

Question

I have a dataframe with the mixed column datatypes that contain strings, arrays, ints. All the arrays are dtype=object.

>>> test = pd.DataFrame({'id': ['a','b','c', 'd'],
            'state': ['Arizona', np.array(['Texas', 'Texas', 'Texas']), 'Texas', np.array(['Texas', 'California'])],
            'zip': [91239, 21939, np.array([12941,13511]), np.array([11111, 11111, 11111])]})
    
>>> test
      id                  state                    zip
    0  a                Arizona                  91239
    1  b  [Texas, Texas, Texas]                  21939
    2  c                  Texas         [12941, 13511]
    3  d    [Texas, California]  [11111, 11111, 11111]

My desired output is to deduplicate arrays wherever they exist and when there are more than one different items in an array, to replace it with a string that says 'Multiple'

desired_output
  id     state       zip
0  a   Arizona     91239
1  b     Texas     21939
2  c     Texas  Multiple
3  d  Multiple     11111

I've tried to follow weird logic where I first create temp columns that count the number of unique items within a column, or that check if all() items in an array match the first indexed item, but these are all breaking. Thanks for any help!