How to select the maximum value from a spark dataframe in a particular time frame

I have a set of records for some shipments. The record set having the columns container_no, origin, destination, shipment_dt and volume.

There are multiple records having the same container_no and having some possibility of that the same container has been shipped on different dates but if the shipment_dt falls within 10 days of time span then check the origin of the container_no, if all the records having different origin then drop all the records having same container_no and falling within the 10 days of time span else select the record with the highest volume.

Pleas Note that: We will decide the 10 days of time span on the basis of the first occurrence of the container_no.
Sample Input:

Expected output:

Origin destination condition of selection data:

I have already written a query to get the time range of ten days but don't know how do I compare the origin and destination and get the records with the highest volume.

Sample Input Query to create dataframe:

val Input_DF = spark.sql("""
    SELECT '12345' AS container_no , 'India' AS origin ,'China' AS destination , '2020-10-10' AS shipment_dt , 20 as volume UNION
    SELECT '12345' AS container_no , 'India' AS origin ,'China' AS destination , '2020-10-10' AS shipment_dt , 30 as volume UNION
    SELECT '12345' AS container_no , 'India' AS origin ,'China' AS destination , '2020-10-12' AS shipment_dt , 10 as volume UNION
    SELECT '12345' AS container_no , 'India' AS origin ,'China' AS destination , '2020-10-25' AS shipment_dt , 20 as volume UNION
    SELECT '12345' AS container_no , 'India' AS origin ,'China' AS destination , '2020-10-26' AS shipment_dt , 10 as volume UNION
    SELECT '12346' AS container_no , 'India' AS origin ,'China' AS destination , '2020-10-15' AS shipment_dt , 20 as volume UNION
    SELECT '12346' AS container_no , 'India' AS origin ,'China' AS destination , '2020-10-16' AS shipment_dt , 20 as volume UNION
    SELECT '12346' AS container_no , 'India' AS origin ,'China' AS destination , '2020-10-17' AS shipment_dt , 50 as volume UNION
    SELECT '12347' AS container_no , 'India' AS origin ,'China' AS destination , '2020-10-18' AS shipment_dt , 20 as volume UNION
    SELECT '12347' AS container_no , 'Nepal' AS origin ,'China' AS destination , '2020-10-19' AS shipment_dt , 21 as volume""")

Input_DF.createOrReplaceTempView("Input_DF")

Query to create a 10 days time frame on data:

val output_df = spark.sql("""
            SELECT
                      B.* ,
                      CASE
                          WHEN from_prev BETWEEN 0 AND 9
                          THEN 1
                          ELSE 0
                      END                     AS recent ,
                      floor(from_first / 10 ) AS recent_group
                  FROM
                      (
                          SELECT
                              A.*,
                              NVL(DATEDIFF(shipment_dt,FIRST(shipment_dt) over(partition BY container_no
                              ORDER BY shipment_dt ASC)) ,0) AS from_first,
                              NVL(DATEDIFF(shipment_dt,lag(shipment_dt,1) over(partition BY container_no
                              ORDER BY shipment_dt ASC)) ,0) from_prev
                          FROM
                              Input_DF A) B 
                  ORDER BY
                    container_no,
                    shipment_dt""")

In the sample input screenshot I have added one extra column to explain that the how one row in different from another records having same container_no but date is different. Thanks in advance.