Groupby/apply behaves differently when grouping column contains tuples
#### Code Sample, a copy-pastable example if possible

This is adapted from the [docs](https://pandas.pydata.org/pandas-docs/stable/groupby.html#returning-a-series-to-propagate-names), just replacing column 'a' with a list of tuples:

```python
import pandas as pd

df = pd.DataFrame({
        'a':  [(0,), (0,), (0,), (0,), (1,), (1,), (1,), (1,), (2,), (2,), (2,), (2,)],
        'b':  [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1],
        'c':  [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
        'd':  [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
        })

def compute_metrics(x):
    result = {'b_sum': x['b'].sum(), 'c_mean': x['c'].mean()}
    return pd.Series(result, name='metrics')

df.groupby('a').apply(compute_metrics)
```
#### Problem description

Without the modification, the return value is a dataframe with the apply-returned Series objects concatenated. With the modification, it is a Series object filled with the individual Series objects.

#### Expected Output

The same behavior with and without modification.

#### Background

The divergence in the behavior is caused by the code in `pandas/core/index.py` introduced in #10703, which was a reaction on #10697. Simply commenting out the if block `if all( isinstance(e, tuple) for e in data ):` solves the issue.