BUG: Hash-function doesn't take upper bits into consideration for PyObjects
- [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.

---

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

```python
import pandas as pd
import numpy as np

def create(M, shift=0):
    lst=[x<<shift for x in range(M)]
    return np.array(lst, dtype=np.object)

#linear running time:
%timeit pd.unique(create(10**3, 0))
%timeit pd.unique(create(10**4, 0))

# quadratic running time:
# for shift=32, the lower 32bits returned 
# by Py_Hash are 0 for 64bit builds
%timeit pd.unique(create(10**3, 32))
%timeit pd.unique(create(10**4, 32))

```

#### Problem description

The times for small integers are: 222 µs vs 1.6 ms, i.e. about factor 10
The times for big integers are: 16.5 ms vs 1.68 s, i.e. about factor 100

#### Expected Output

Also for big integers, the factor should be about 10 (i.e. linear) and not quadratic.

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

<details>

INSTALLED VERSIONS
------------------
commit           : 824869f5ee9f0fcc5b082d4a375395b620cb4667
python           : 3.7.3.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.4.0-53-generic
Version          : #74-Ubuntu SMP Fri Dec 2 15:59:10 UTC 2016
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.2.0.dev0+1033.g824869f
numpy            : 1.19.2
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.2.2
setuptools       : 49.6.0.post20200814
Cython           : 0.29.21
pytest           : 5.4.3
hypothesis       : 5.36.0
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.18.1
pandas_datareader: None
bs4              : 4.9.1
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : 1.2.1
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : 0.50.1

</details>
