BUG: Different initialization methods lead to different dtypes (DataFrame)
- [x] I have checked that this issue has not already been reported.

- [x] I have confirmed this bug exists on the latest version of pandas.

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

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#### Code Sample, a copy-pastable example

```python
import pandas as pd

df1 = pd.DataFrame(columns=["a", "b", "c"])
print(df1.groupby("a").sum().columns)
# => Index([], dtype='object')
df2 = pd.DataFrame({"a": [], "b": [], "c": []})
print(df2.groupby("a").sum().columns)
# => Index(['b', 'c'], dtype='object')

```

#### Problem description

`groupby`-ing and `sum`ming an empty dataframe led to dropped columns (`df1` above); this doesn't occur with non-empty dataframes. This changing the columns of a dataframe based on its content is counter-intuitive and leads to key errors. The expected behaviour is shown above with `df2`, and the fact that two empty dataframes show different behaviours when grouped and summed suggests that this isn't intended behaviour.

#### Expected Output

#### Output of the above snippet:

<details>

Index([], dtype='object')
Index(['b', 'c'], dtype='object')

</details>
