BUG: Integer Capacity Higher Than Necessary in Mappings from Pandas to SQLAlchemy Types 
- [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.

#### Problem description

Currently, to map the Pandas data type to a SQLAlchemy type: https://github.com/pandas-dev/pandas/blob/master/pandas/io/sql.py#L1066-L1069, the code reads:

```python
elif col_type == "integer":
  if col.dtype == "int32":
    return Integer
  else:
    return BigInteger
```

This means integers of a capacity less than 32-bit are written to the database as if they were 64-bit. An example where one might get smaller integers is through the use of [pd.to_numeric](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_numeric.html) and `downcast=True`.

Since there is already a check for the `col_type == "integer"`, I think switching the `col.dtype` check to this could be a possible fix:

```python
elif col_type == "integer":
  if col.dtype == "int64":
    return BigInteger
  else:
    return Integer
```

But I'm not sure how to get started with an official PR or if this is a sane thing to do.

For context (this part is not an issue for Pandas, just explaining my interest in this issue), I discovered this when using the [df.to_sql](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_sql.html) method to persist datasets to a Postgres database. From there, I use [Postgraphile](https://www.graphile.org/postgraphile/) to auto-generate a GraphQL endpoint. I found that the `BigInteger` type ends up resolving as a string, because Javascript can't represent such large numbers [safely](https://github.com/brianc/node-pg-types/issues/28). This would be fine if the source data warranted the high-capacity, but for me it often doesn't.

#### Expected Output

That the column types that get written to a new table in the database more tightly match their data types in Pandas.

#### Workarounds

If using [df.to_sql](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_sql.html), one could set the `dtype` argument but this can be unruly when there are many columns in the DataFrame.

For now, I have this helper method to downcast integer columns to their lowest capacity, but then back to 32-bit to persist them to the database with the desired column type:

```python
def cast_to_int32(df):
    # Downcast to the lowest possible representation.
    for col in df.select_dtypes(include=['int']).columns.values:
        df[col] = pd.to_numeric(df[col], downcast='integer')
    # Upcast back to 32-bit (since that's what gets persisted correctly)
    for col in df.select_dtypes(include=['int8', 'int16']).columns.values:
        df[col] = df[col].astype('int32')
    return df
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.8.2.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.4.0-39-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.5
numpy            : 1.19.0
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 44.0.0
Cython           : None
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           : None
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : 1.3.17
tables           : None
tabulate         : 0.8.7
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
numba            : None

</details>
