How to specify nested partitions in merge query while trying to merge incremental data with a base table?

Question

I am trying to merge a dataframe that contains incremental data into my base table as per the databricks documentation.

base_delta.alias('base') \
    .merge(source=kafka_df.alias('inc'), 
           condition='base.key1=ic.key1 and base.key2=inc.key2') \
    .whenMatchedUpdateAll() \
    .whenNotMatchedInsertAll() \
    .execute()

The above operation is working fine but it takes lot time as expected since there are lot of unwanted partitions that are being scanned. I came across a databricks documentation here, a merge query with partitions specified in it.

Code from that link:

spark.sql(s"""
     |MERGE INTO $targetTableName
     |USING $updatesTableName
     |ON $targetTableName.par IN (1,0) AND $targetTableName.id = $updatesTableName.id
     |WHEN MATCHED THEN
     |  UPDATE SET $targetTableName.ts = $updatesTableName.ts
     |WHEN NOT MATCHED THEN
     |  INSERT (id, par, ts) VALUES ($updatesTableName.id, $updatesTableName.par, $updatesTableName.ts)
 """.stripMargin)

The partitions are specified in the IN condition as 1,2,3... But in my case, the table is first partitioned on COUNTRY values USA, UK, NL, FR, IND and then every country has partition on YYYY-MM Ex: 2020-01, 2020-02, 2020-03 How can I specify the partition values if I have nested structure like I mentioned above ? Any help is massively appreciated.