PERF: optimize DataFrame.sparse.from_spmatrix performance
This optimizes `DataFrame.sparse.from_spmatrix` performance, using an approach proposed by @jorisvandenbossche in https://github.com/pandas-dev/pandas/issues/32196#issuecomment-600824238

Bulds on top of https://github.com/pandas-dev/pandas/pull/32821 and adds another ~4.5x speed up in addition to that PR. Benchmarks for run time (in seconds) are done by running `pd.DataFrame.sparse.from_spmatrix` on a random sparse CSR array of given n_samples, n_features with a density=0.01:
```
label                     PR   master Speed-up master/PR                                                                               
n_samples n_features                                    
100       100000      2.3624  10.1247              4.29x
10000     10000       0.2391   1.1037              4.62x
100000    100         0.0031   0.0134              4.32x
```

with the benchmarking code below,
<details>

```py
import pandas as pd
import numpy as np
import scipy.sparse

from neurtu import timeit, delayed


def bench_cases():
    for n_samples, n_features in [(100, 100000), (10000, 10000), (100000, 100)]:
        X = scipy.sparse.rand(
            n_samples, n_features, random_state=0, density=0.01, format="csr"
        )
        tags = {"n_samples": n_samples, "n_features": n_features, "label": "PR"}

        yield delayed(pd.DataFrame.sparse.from_spmatrix, tags=tags.copy())(X)


res = timeit(bench_cases())

res = (
    res.reset_index()
    .set_index(["n_samples", "n_features", "label"])["wall_time"]
    .unstack(-1)[["PR"]]
    .round(4)
)
# res["master"] = np.array([10.1247, 1.1037, 0.0134])

# res["Speed-up master/PR"] = (res["master"] / res["PR"]).round(2).astype("str") + "x"
print(res)
```

</details>

Closes https://github.com/pandas-dev/pandas/issues/32196 although further optimization might be possible. Around 90% of remaining run time happens in `DataFrame._from_arrays` which goes deeper into pandas internals. Maybe some checks could be disabled there, but that looks less straightforward.