How to explode multiple columns, different types and different lengths?

I've got a DF with columns of different time cycles (1/6, 3/6, 6/6 etc.) and would like to "explode" all the columns to create a new DF in which each row is a 1/6 cycle.

from pyspark import Row 
from pyspark.sql import SparkSession 
from pyspark.sql.functions import explode, arrays_zip, col

spark = SparkSession.builder \
    .appName('DataFrame') \
    .master('local[*]') \
    .getOrCreate()

df = spark.createDataFrame([Row(a=1, b=[1, 2, 3, 4, 5, 6], c=[11, 22, 33], d=['foo'])])

|  a|                 b|           c|    d|
+---+------------------+------------+-----+
|  1|[1, 2, 3, 4, 5, 6]|[11, 22, 33]|[foo]|
+---+------------------+------------+-----+

I'm doing the explode:

df2 = (df.withColumn("tmp", arrays_zip("b", "c", "d"))
       .withColumn("tmp", explode("tmp"))
       .select("a", col("tmp.b"), col("tmp.c"), "d"))

But the output is not what I want:

|  a|  b|   c|    d|
+---+---+----+-----+
|  1|  1|  11|[foo]|
|  1|  2|  22|[foo]|
|  1|  3|  33|[foo]|
|  1|  4|null|[foo]|
|  1|  5|null|[foo]|
|  1|  6|null|[foo]|
+---+---+----+-----+

I would want it to look like this:

|  a|  b|  c|  d|
+---+---+---+---+
|  1|  1| 11|foo|
|   |  2|   |   |
|   |  3| 22|   |
|   |  4|   |   |
|   |  5| 33|   |
|   |  6|   |   |
+---+---+---+---+

I am new to Spark and from the start I've got complicated topics ! :)

Update: Maybe someone has a solution without usage of UDFs?

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