PERF: allow to skip validation/sanitization in DataFrame._from_arrays
For cases where you know to have valid data (eg you just created them yourself, or they are already validated), it can be useful to skip the validation checks when creating a DataFrame from arrays.

Use case is for example https://github.com/pandas-dev/pandas/pull/32825

From investigating https://github.com/pandas-dev/pandas/issues/32196#issuecomment-600824238

@rth this gives another 20% improvement on the dataframe creation part. Together with https://github.com/pandas-dev/pandas/pull/32856, it gives a bit more than a 2x improvement on the dataframe creation part (once the sparse arrays are created)
```
In [1]: arrays = [pd.arrays.SparseArray(np.random.randint(0, 2, 1000), dtype="float64") for _ in range(10000)] 
   ...: index = pd.Index(range(len(arrays[0])))   
   ...: columns = pd.Index(range(len(arrays)))

In [2]: %timeit pd.DataFrame._from_arrays(arrays, index=index, columns=columns)   
119 ms ± 3.52 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [3]: %timeit pd.DataFrame._from_arrays(arrays, index=index, columns=columns, verify_integrity=False)    
98.1 ms ± 713 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
``` Very nice, thanks @jorisvandenbossche ! It's good that is applies to extension arrays in general not just sparse frames. Though I guess the use case of a very large number of columns (>10k) is less common outside of sparse. Actually my comment was more about https://github.com/pandas-dev/pandas/pull/32856, got confused in your multiple performance improvement PRs :) This is nice too!  I think your comment applies to both ;)  Nice docstring. 😄  Doesn't matter too much, can be added either here or there. 
And I would also like to add `verify_integrity` to the benchmark that I am adding in https://github.com/pandas-dev/pandas/pull/32856 SGTM. I think this can go in since CI is passing :)