BUG: 2D ndarray of dtype 'object' is always copied upon construction
- [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.

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

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

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

```python
import numpy as np                             
import pandas as pd    
                                                                                                                                                                                                                                                
a = np.array(['a', 'b'], dtype='object')                                                                                                                                                                                                                                                                                                                                                    
df = pd.DataFrame(a)                                                                                                                                                                                       

assert np.shares_memory(df.values, a)  # True         
                                                                                                                                                                      
b = np.array([['a', 'b'], 
              ['c', 'd']], dtype='object')                                                                                                                                                                                 
df2 = pd.DataFrame(b)                                                                                                                                                                                     

assert np.shares_memory(df2.values, b)  # False
```

#### Problem description
This issue addresses the TODO in `pandas.core.internals.construction.py`, line 250 on master at time of writing. This was introduced by #26825. It's quite annoying, I'm trying to use DataFrame as an interface to access my dtype object numpy array, but I can't without copying it in first.

#### Expected Output
Exit code 0.

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

<details>

INSTALLED VERSIONS
------------------
commit           : b5958ee1999e9aead1938c0bba2b674378807b3d
python           : 3.6.10.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.6.0
Version          : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_GB.UTF-8
LOCALE           : en_GB.UTF-8

pandas           : 1.1.5
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 46.1.3
Cython           : 0.29.14
pytest           : 5.3.5
hypothesis       : 5.4.1
sphinx           : 2.3.1
blosc            : None
feather          : None
xlsxwriter       : 1.2.8
lxml.etree       : 4.5.0
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.11.1
IPython          : 7.12.0
pandas_datareader: None
bs4              : 4.8.2
bottleneck       : 1.3.1
fsspec           : 0.6.2
fastparquet      : None
gcsfs            : None
matplotlib       : 3.2.1
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 3.0.3
pandas_gbq       : None
pyarrow          : None
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.13
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.2.0
numba            : 0.48.0

</details>
