explode an spark array column to multiple columns sparksql

I have a column which has type Value defined like below

val Value: ArrayType = ArrayType(
  new StructType()
  .add("unit", StringType)
  .add("value", StringType)
)

and data like this

[[unit1, 25], [unit2, 77]]
[[unit2, 100], [unit1, 40]]
[[unit2, 88]]
[[unit1, 33]]

I know spark sql can use functions.explode to make the data become multiple rows, but what i want is explode to multiple columns (or the 1 one column but 2 items for the one has only 1 item).

so the end result looks like below

unit1 unit2
25	77
40	100
value1 88
33 value2

How could I achieve this?

addtion after initial post and update
I want to get result like this (this is more like my final goal).

transformed-column
[[unit1, 25], [unit2, 77]]
[[unit2, 104], [unit1, 40]]
[[unit1, value1], [unit2, 88]]
[[unit1, 33],[unit2,value2]]

where value1 is the result of applying some kind of map/conversion function using the [unit2, 88]
similarly, value2 is the result of applying the same map /conversion function using the [unit1, 33]

I solved this problem using the map_from_entries as suggested by @jxc, and then used UDF to convert the map of 1 item to map of 2 items, using business logic to convert between the 2 units.

one thing to note is the map returned from map_from_entries is scala map. and if you use java, need to make sure the udf method takes scala map instead.

ps. maybe I did not have to use map_from_entries, instead maybe i could make the UDF to take array of structType