您可以编写一个自定义函数来查找df1中的日期，然后将其应用于df2['Col_2']：

import pandas as pd

df1 = pd.DataFrame({
    'Col_1': pd.to_datetime([
        '2023-01-02',
        '2023-01-03',
        '2023-01-04',
        '2023-01-05',
        '2023-01-08'
    ])
})

df2 = pd.DataFrame({
    'Col_2': pd.to_datetime([
        '2023-01-02 9:00',
        '2023-01-02 20:00',
        '2023-01-03 1:00',
        '2023-01-04 9:00',
        '2023-01-05 16:00',
        '2023-01-06 9:00',
        '2023-01-08 12:00'])
})

## create a function that takes a datetime as an input, 
## and returns the correct datetime from df1 'Col_1' as a reference
def get_date_from_df1(ts, df1):
    ts_in_df1 = sum(df1['Col_1'] == ts.floor(freq='d'))
    ## get index of the the ts:
    if ts_in_df1 == 1:
        if ts.hour < 12:
            idx = df1.loc[df1['Col_1'] == ts.floor(freq='d')].index[0]
        elif (ts.hour == 12) & (ts.minute == 0):
            idx = df1.loc[df1['Col_1'] == ts.floor(freq='d')].index[0]
            if idx + 1 > df1.index[-1]:
                return ts.ceil(freq='d')
        else:
            idx = df1.loc[df1['Col_1'] == ts.floor(freq='d')].index[0] + 1

    elif sum(df1['Col_1'] > ts.floor(freq='d')) == 1:
        idx = df1.loc[df1['Col_1'] > ts.floor(freq='d')].index[0]
    
    ## if the date isn't found in df1, we return None (to avoid an error)
    else:
        return None

    return df1['Col_1'].iloc[idx]

df2['Col_2'] = df2['Col_2'].apply(lambda ts: get_date_from_df1(ts, df1))

结果：

>>> df2
       Col_2
0 2023-01-02
1 2023-01-03
2 2023-01-03
3 2023-01-04
4 2023-01-08
5 2023-01-08
6 2023-01-09