PERF: fix performance regression in memory_usage(deep=True) for object dtype
- [x] closes #33012  
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry

The pull request is to update lib.memory_usage_of_objects from taking self.arrays to self._values. An ASV included to benchmark with and without object-dtype columns.

Before:
![Screen Shot 2020-03-28 at 11 57 22 AM](https://user-images.githubusercontent.com/33635204/77827342-6efe3380-70eb-11ea-9f32-6fd835cca76f.png)

After:
![Screen Shot 2020-03-28 at 11 56 42 AM](https://user-images.githubusercontent.com/33635204/77827344-71608d80-70eb-11ea-9745-e8754f6aebf2.png)
@jbrockmendel thoughts? LGTM.  @jorisvandenbossche has stronger opinions than i do on when to use `.array` @jbrockmendel This is not about an opinion on when to use `.array` or not, this is about a cython routine needing a numpy array and not an EA. 
(so the alternative to `_values` would be `extract_array(... , extract_numpy=True)`, but since we already know we are object dtype here, it should be fine to use `_values`) @neilkg Can you please merge master?
This should solve the failing test.

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https://dev.pandas.io/docs/development/contributing.html#updating-your-pull-request > @neilkg Can you please merge master?
> This should solve the failing test.

@MomIsBestFriend done Thanks @neilkg!  this was tagged for 1.04? hmm

not sure if we are doing that but no harm
i guess (though the release note will need to be moved)