REGR: Performance of DataFrame reduction ops with axis=1
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Siphoning off the largest impact from #52083.

```
shape = 250_000, 100
mask = pd.DataFrame(np.random.randint(0, 1, size=shape), dtype=pd.BooleanDtype())
%timeit mask.transpose()
# 4.28 s ± 12.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)  # <-- main
# 1.12 s ± 19.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)  # <-- PR
```
I've been thinking about this and #52083.  This is a workaround for lack of 2D support in MaskedArray (not unlike #52261).  Maybe its time to revisit actually supporting 2D in MaskedArray? > I've been thinking about this and #52083. This is a workaround for lack of 2D support in MaskedArray (not unlike #52261). Maybe its time to revisit actually supporting 2D in MaskedArray?

Agreed this is a workaround, no opposition to 2D for masked dtypes. 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.