BUG: Columns/Series with np.array() stop working in v1.2+
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- [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
## Pandas <1.2.0

import numpy as np
import pandas as pd

data = pd.DataFrame(
    {
        "admin": [0.0, 0.15, 0.15],
        "rate": [
            np.array([[0.5, 0.25, 0.25], [1.2, 0.6, 0.3]]),
            np.array([[0.0, 0.75, 0.25], [5.5, 0.4, 0.7]]),
            np.array([[1.0, 0, 0], [1.0, 0, 0]]),
        ],
        "distrib": [
            np.array([0.0, 1.0, 0.0]),
            np.array([0.0, 0.0, 1.0]),
            np.array([1.0, 0, 0]),
        ],
    }
)

data["base"] = data["admin"] * data["rate"]
data["final"] = data["base"] * data["distrib"] * 2

print(data["final"])
```

Outputs:

```
0       [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
1    [[0.0, 0.0, 0.075], [0.0, 0.0, 0.21]]
2       [[0.3, 0.0, 0.0], [0.3, 0.0, 0.0]]
Name: final, dtype: object
```

#### Problem description

This is kind-of a bug, kind-of a regression, kind-of a feature request.

Prior to version `v1.2.0` we could have Numpy N-d arrays within cells of the DataFrame. We could apply math to them, math columns together, etc. It worked *fairly* well—with a few small hiccups. But it was workable.

With `v1.2.0` Pandas got much more strict on having only scalar values in Series. I understand why—that’s the primary purpose of Pandas DataFrames. But it completely breaks our use case.

Our use case is computing costs for leases within large commercial properties. The costs are distributed into cost pools, matrixes of interior/exterior expenses (as an example). Our cost pools are `3x3` NP arrays. We need to take those cost pools and multiply them by admin fees & redistribute them based on lease conditions etc.

| lease_id | admin_fee | rate_cost_pool |
| --- | --- | --- |
| 1 | 0.15 | [ [4.5,0.5,1.5], [4.5,0.5,1.5], [4.5,0.5,1.5] ] |
| 2 | 0.10 |  [ [0.0,0.5,1.5], [0.343,0.5,1.5], [4.5,0.5,2.87] ] |

*Example: multiply each `rate_cost_pool` by their `admin_fee`, etc.*

**With Pandas v1.2.0 this stopped working.** Getting “can only perform ops with numeric values“ `TypeErrors`, etc.
 
I thought maybe making a new Dtype would allow me to somehow force Pandas to think my NDArrays are “scalar” but couldn’t figure that out. I got close by using `Tensor` from this library:
https://github.com/CODAIT/text-extensions-for-pandas

#### Expected Output

I hope there’s a way to get this working with newer Pandas versions. It’s not sustainable to keep our Pandas version out-of-date.

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

<details>

INSTALLED VERSIONS
------------------
commit           : b5958ee1999e9aead1938c0bba2b674378807b3d
python           : 3.8.11.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 20.5.0
Version          : Darwin Kernel Version 20.5.0: Sat May  8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : en_US.UTF-8
LANG             : en_US
LOCALE           : en_US.UTF-8

pandas           : 1.1.5
numpy            : 1.21.0
pytz             : 2021.1
dateutil         : 2.8.2
pip              : 21.0.1
setuptools       : 57.0.0
Cython           : None
pytest           : 6.2.4
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : 1.4.4
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.9.1 (dt dec pq3 ext lo64)
jinja2           : None
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : 1.3.2
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : 2.7.3
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : 1.4.21
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
