BUG: ValueError in read_csv when dtype='string' and parse_dates is present
- [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.

---

**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 pandas as pd
import datetime
df = pd.DataFrame({'a': [1], 'b': [datetime.datetime.now()]})
df.to_csv('test.csv', index=None)
pd.read_csv('test.csv', dtype='string', parse_dates=['b'])
```

#### Problem description

`ValueError: not all elements from date_cols are numpy arrays`

#### Expected Output

Data frame with all columns as strings except ones specified in  parse_dates optional argument.

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.8.1.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.15.0-22-generic
machine          : x86_64
processor        : 
byteorder        : little
LC_ALL           : en_US.UTF-8
LANG             : en_US.UTF-8
LOCALE           : None.None

pandas           : 1.0.1
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 46.1.3
Cython           : 0.29.15
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.5.0
html5lib         : None
pymysql          : None
psycopg2         : 2.8.4 (dt dec pq3 ext lo64)
jinja2           : 2.11.2
IPython          : 7.13.0
pandas_datareader: None
bs4              : 4.9.0
bottleneck       : 1.3.2
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.5.0
matplotlib       : 3.1.3
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.16.0
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.13
tables           : None
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
numba            : 0.48.0

</details>
