BUG: to_sql performance decreases dramatically with more than 10 columns (SQL Server)
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- [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.

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Found an interesting issue regarding the performance of to_sql() in combination with SQL Server.

OS: Centos/Redhat
DB Driver: ODBC Driver 17 for SQL Server
DB Server (remote): SQLServer 12

Performance:
34484 rows x 10 columns => >10k records per second

34484 rows x 12 columns => ~500 records per second

I tested it on two different Servers and DB's and the type of data doesn't seem to matter (all dtypes are object), the drop happens always if the df is havin more than 10 Columns

```python
    engine = sqlalchemy.create_engine("mssql+pyodbc:///?odbc_connect=%s?charset=utf8" % params, fast_executemany=True)
    sqlalchemy_connection = engine.connect()
    ....
   df.to_sql(name='TEST', con=con, if_exists='append', index=False)

```

(Tested it now with postgresql, the "bug" does not appear there, get around 14k/sec)
