PERF: avoid going through .values for reindexing both index/columns for ArrayManager
xref https://github.com/pandas-dev/pandas/issues/39146

This fastpath calls `self.values` and thus is only a fastpath for BlockManager (we only get here with all columns having the same dtype).
I've been looking at this code path as, for reasons related to the CI failures, it appears fragile.  e.g. in TestDataFrameSelectReindex.test_reindex_datetimelike_to_object setting `df["b"] = 9` (following `df = ser.unstack()`) breaks similarly.  Another degree of separation xref #42921. It also seems that our deprecation is not working if you only reindex the rows (and not the rows/columns together):

```
In [1]: arr = date_range("2016-01-01", periods=6).values.reshape(3, 2)
   ...: df = DataFrame(arr, columns=["A", "B"], index=range(3))
   ...: ts = df.iloc[0, 0]
   ...: fv = ts.date()

In [2]: df.reindex(index=range(4), columns=["A", "B", "C"], fill_value=fv)
<ipython-input-2-fdba941b8324>:1: FutureWarning: Using a `date` object for fill_value with `datetime64[ns]` dtype is deprecated. In a future version, this will be cast to object dtype. Pass `fill_value=Timestamp(date_obj)` instead.
  df.reindex(index=range(4), columns=["A", "B", "C"], fill_value=fv)
Out[2]: 
           A          B          C
0 2016-01-01 2016-01-02 2016-01-01
1 2016-01-03 2016-01-04 2016-01-01
2 2016-01-05 2016-01-06 2016-01-01
3 2016-01-01 2016-01-01 2016-01-01

In [3]: df.reindex(index=range(4), fill_value=fv)
...
TypeError: value should be a 'Timestamp' or 'NaT'. Got 'date' instead.
```

The second should also work and trigger the deprecation warning (the tests here are running into this because I am now basically taking the reindex path in separate steps for AM).

Which is probably indeed the same as https://github.com/pandas-dev/pandas/issues/42921 > It also seems that our deprecation is not working

IIRC this code path goes through maybe_promote, which doesn't _quite_ match the DTA behavior.  There was some work a few months ago that brought them closer into alignment, but off the top of my head I don't remember how far that got. seems reasonable can you merge master This pull request is stale because it has been open for thirty days with no activity. Please [update](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#updating-your-pull-request) and respond to this comment if you're still interested in working on this. Thanks for the pull request, but it appears to have gone stale. If interested in continuing, please merge in the main branch, address any review comments and/or failing tests, and we can reopen.