Index vs. MultiIndex behavior on slices of DataFrames (and Series)
#### Code Sample, a copy-pastable example if possible

```python
import random as rd

df_single = pd.DataFrame(data={"Col1": rd.sample(range(1, 100), 25)}, index=rd.sample(range(1, 100), 25))
single_slice = df_single[:9]
single_slice.index

df_multi = pd.DataFrame(data={"Col1": rd.sample(range(1, 100), 25)}, index=[["a" for _ in range(10)] + ["b" for _ in range(15)], rd.sample(range(1, 100), 25)])
multi_slice = df_multi[:9]
multi_slice.index

```
#### Problem description

When slicing the single index df, the index of the slice is returned in the order it appears in the single df. However, when slicing the multi index df, the index attribute of the slice will return the index of the complete (unsliced) df.

This seems inconsistent to me and is quite inconvenient in several different cases. Is this intentional? (if yes, what is the reasoning?)

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#### Expected Output

#### Output of ``pd.show_versions()``

<details>

[paste the output of ``pd.show_versions()`` here below this line]

</details>
