BUG: pd.json_normalize on a column loses rows that have an empty list for that column
- [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

We lose the first row when we normalize the "sections" field:
```python
import pandas as pd

import pandas as pd
df = pd.DataFrame({'id': [1, 2, 3],
                   'sections': [[], [{"id": 1, "answers": [{"id": 1, "value": 10, "question_id": 1}]}], [{"id": 2, "answers": []}]]
                   })
df2 = pd.json_normalize(df.to_dict(orient="records"), meta=["id"], record_path="sections", record_prefix="section_")
df2
```

The problem would then impact the section_answers field if that column gets normalized (we end up with only a single row for ID 2)

`pd.json_normalize(df2.to_dict(orient="records"), meta=["id", "section_id"], record_path="section_answers", record_prefix="answer_")`

#### Problem description

I am losing the first row when I normalize the "sections" field in my example. This is because the field contains an empty list.

The end result is that I have a dataframe with fewer unique IDs than I started out with which is not desirable since the distinct count of these IDs are used as a metric.

#### Expected Output

I would expect one at least one row per meta column that I passed to pd.json_normalize. I do not think that I should lose the row ID 1 without some type of warning at least:

![image](https://user-images.githubusercontent.com/12517855/92624141-becb6280-f2f9-11ea-919d-536ef7a7d9a3.png)

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

<details>

INSTALLED VERSIONS
------------------
commit           : d9fff2792bf16178d4e450fe7384244e50635733
python           : 3.8.5.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.3.0-1028-azure
Version          : #29~18.04.1-Ubuntu SMP Fri Jun 5 14:32:34 UTC 2020
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : en_US.UTF-8
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.1.0
numpy            : 1.19.1
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.2.2
setuptools       : 49.6.0.post20200814
Cython           : 0.29.21
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.5 (dt dec pq3 ext lo64)
jinja2           : 2.10.3
IPython          : 7.17.0
pandas_datareader: None
bs4              : 4.9.1
bottleneck       : 1.3.2
fsspec           : 0.7.4
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.0
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 3.0.4
pandas_gbq       : None
pyarrow          : 1.0.0
pytables         : None
pyxlsb           : 1.0.6
s3fs             : None
scipy            : 1.5.2
sqlalchemy       : 1.3.19
tables           : 3.6.1
tabulate         : 0.8.7
xarray           : None
xlrd             : 1.2.0
xlwt             : None
numba            : 0.48.0

</details>
