BUG: Pandas 1.0.5 → 1.1.0 behavior change on DataFrame.apply() where fn returns np.ndarray
- [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.

---

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

```python
print(type(
  pd.DataFrame([['foo']])
  .apply(lambda col: np.array('bar'))
  .iloc[0]))
```

#### Output of Pandas 1.0.5

```
<class 'str'>
```

#### Output of Pandas 1.1.0

```
<class 'numpy.ndarray'>
```

It is not clear to me if this behaviour change is intended or not. I couldn't find anything obvious in the [release notes](https://pandas.pydata.org/pandas-docs/stable/whatsnew/v1.1.0.html).

I discovered it because it broke my code, which returns the output of `DataFrame.unique().squeeze()` (with the intent of extracting a scalar) in an `apply()` func. Arguably my code is wrong, because `DataFrame.unique()` returns an `np.ndarray`, and calling `squeeze()` on that always returns an ndarray, never a scalar - the correct call would have been `item()`. Up until now, Pandas would "hide" the bug because it would take the one-element ndarray and treat it as a scalar. Not anymore, it seems. My code ultimately fails downstream of the call as it tries to hash the resulting string, which doesn't work if the string is actually a 1-element ndarray.

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

<details>

INSTALLED VERSIONS
------------------
commit           : d9fff2792bf16178d4e450fe7384244e50635733
python           : 3.6.9.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.19.104+
Version          : #1 SMP Wed Feb 19 05:26:34 PST 2020
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.1.0
numpy            : 1.19.1
pytz             : 2018.9
dateutil         : 2.8.1
pip              : 19.3.1
setuptools       : 49.2.0
Cython           : 0.29.21
pytest           : 3.6.4
hypothesis       : None
sphinx           : 1.8.5
blosc            : None
feather          : 0.4.1
xlsxwriter       : None
lxml.etree       : 4.2.6
html5lib         : 1.0.1
pymysql          : None
psycopg2         : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2           : 2.11.2
IPython          : 5.5.0
pandas_datareader: None
bs4              : 4.6.3
bottleneck       : 1.3.2
fsspec           : 0.7.4
fastparquet      : None
gcsfs            : None
matplotlib       : 3.2.2
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 2.5.9
pandas_gbq       : 0.11.0
pyarrow          : 0.14.1
pytables         : None
pyxlsb           : None
s3fs             : 0.4.2
scipy            : 1.4.1
sqlalchemy       : 1.3.18
tables           : 3.4.4
tabulate         : 0.8.7
xarray           : 0.15.1
xlrd             : 1.1.0
xlwt             : 1.3.0
numba            : 0.48.0

</details>
