When using "unnestDDBJson" it's forced to resolve and flatten the schema, I think you are will need to avoid that in your case and do it yourself after you have resolved the type.

Apologies, I would have made a comment but I do not have sufficient reputation. I was able to make the solution that Guillermo AMS provided work within AWS Glue, but it did require two changes:

• The "jdbc" format was unrecognized (the provided error was: "py4j.protocol.Py4JJavaError: An error occurred while calling o79.load. : java.lang.ClassNotFoundException: Failed to find data source: jbdc. Please find packages at http://spark.apache.org/third-party-projects.html") -- I had to use the full name: "org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider"
• The query option was not working for me (the provided error was: "py4j.protocol.Py4JJavaError: An error occurred while calling o72.load. : java.sql.SQLSyntaxErrorException: ORA-00911: invalid character"), but fortunately, the "dbtable" option supports passing in either a table or a subquery -- that is using parentheses around a query.

In my solution below I have also added a bit of context around the needed objects and imports.
My solution ended up looking like:

from awsglue.context import GlueContext
from pyspark.context import SparkContext

glue_context = GlueContext(SparkContext.getOrCreate())

tmp_data_frame = glue_context.spark_session.read\
  .format("org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider")\
  .option("url", jdbc_url)\
  .option("user", username)\
  .option("password", password)\
  .option("dbtable", "(select * from test where id<100)")\
  .load()