How to change datetime string into timestamp[us] when reading Json data by Spark

I have some data stored in JSON format like this:

{
  "id":1,
  "time":"2023-01-01 12:34:56"
}

And also I have a Apache Hudi table with same columns. The schema of hudi table was (read by pyarrow):

id: int64
time: timestamp[us, tz=UTC]

I try to use Spark to merge the JSON data into Hudi table:

with SparkSession.builder.getOrCreate() as spark:
    df = spark.read.json('path/to/json/files')
    df.createOrReplaceTempView('upsert_data')
    spark.sql(f"CREATE TABLE snapshot USING hudi LOCATION 'path/to/hudi/table'")
    sql_upsert='''MERGE INTO snapshot AS target
                    USING upsert_data AS source
                    ON source.id = target.id
                  WHEN MATCHED THEN UPDATE SET target.time=source.time
                  WHEN NOT MATCHED THEN INSERT (id, time) values (source.id, source.time)'''
    spark.sql(sql_upsert)

But after the data was merged, the time value in Hudi table become 56019-01-08 12:34:56. I think maybe that's because Spark automatically change the time string into timestamp[ns], which was different from Hudi table's timestamp[us]. How can I solve this problem?

I search the PySpark document but didn't find any functions/settings could directly achieve this. Finally I use cast(to_timestamp(time) as long)/1000 to manually change the time unit from nanosecond to microsecond