BUG: pd.read_sql is incorrectly reading long int when connecting to Teradata

[IzidoroBaltazar]

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd
import numpy as np

query = """
SELECT
    longID
FROM teradata_table
WHERE
longID = 305184080441754059;
"""

dtype = {
    "QueryID": np.uint64,
}

with teradatasql.connect(
    host=HOST, user=USERNAME, password=PASSWORD, logmech="LDAP"
) as connect:
    df = pd.read_sql(query, connect, dtype=dtype)

df.head()
# pandas returns longID 305184080441754048 - close but not quite 305184080441754059

Issue Description

We have trouble when pulling long longID with 18 digits pandas are incorrectly reading the Teradata value. I also tried using cast(longID as decimal(18, 0)) to help Pandas understand the type of longID. So far I haven't found a solution how to fix the problem - incorrect value read.
We are using teradatasql version 20.0.0.24 we can confirm that teradatasql is working correctly as it gives us the value below when using query specified above: Decimal('305184080441754059')

with teradatasql.connect(
    host=HOST, user=USERNAME, password=PASSWORD, logmech="LDAP"
) as con:
    with con.cursor() as cur:                    
        cur.execute(query)
        for row in cur:
            print(f"{row}")

^ this works as expected - so we assume that Teradata SQL library is working correctly.

Expected Behavior

We expect to see 305184080441754059 as longID in the pandas dataframe.

Installed Versions

INSTALLED VERSIONS ------------------ commit : 2cc3762 python : 3.12.9 python-bits : 64 OS : Linux OS-release : 4.18.0-477.15.1.el8_8.x86_64 Version : #1 SMP Thu Jul 20 11:31:48 PDT 2023 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 : 2.3.0
numpy : 1.26.4
pytz : 2025.1
dateutil : 2.9.0.post0
pip : 25.0.1
Cython : None
sphinx : None
IPython : 8.32.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.13.3
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : 2024.11.0
fsspec : 2025.5.1
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.5
lxml.etree : 5.4.0
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
psycopg2 : 2.9.10
pymysql : None
pyarrow : 20.0.0
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.15.3
sqlalchemy : 2.0.38
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : 0.23.0
tzdata : 2025.2
qtpy : None
pyqt5 : None