Possible performance regression in Series.xs() and DataFrame.xs() with MultiIndex.
Performance of `Series.xs()` when getting values at specified index and the **first level** of a large dataset seems to have regressed significantly, after pandas was upgraded to v1.0.5 from v0.25.3.  So is `DataFrame.xs()`.

code:
```python
import numpy as np
import pandas as pd
n=1e4
data = pd.Series(np.arange(n**2), index=pd.MultiIndex.from_product([np.arange(n), -np.arange(n)]))
%timeit data.xs(100, level=0) #first level
%timeit data.xs(-100, level=1) #second level
```
outcome
```python
## 0.25.3
%timeit data.xs(100, level=0)
571 µs ± 1.79 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%timeit data.xs(-100, level=1)
193 ms ± 947 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)


## 1.0.5
%timeit data.xs(100, level=0)
683 ms ± 3.35 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)  

%timeit data.xs(-100, level=1)
197 ms ± 2.42 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) 
```