Parse json with same key to different columns

Question

Schema of my json is next

and field Reports_Rows_Rows_Cells if it not null looks like next:

[Row(Attributes=[Row(Id='account', Value='bd9e85e0-0478-433d-ae9f-0b3c4f04bfe4')], Value='Business Bank Account'),
 Row(Attributes=[Row(Id='account', Value='bd9e85e0-0478-433d-ae9f-0b3c4f04bfe4')], Value='10105.54'),
 Row(Attributes=[Row(Id='account', Value='bd9e85e0-0478-433d-ae9f-0b3c4f04bfe4')], Value='4938.48')]

What I want is to create table which has all above columns and column Reports_Rows_Rows_Cells should look like

-------- |-------|Reports_Rows_Rows_Cells_Value | Reports_Rows_Rows_Cells_Value | Reports_Rows_Rows_Cells_Value|
                 | Business Bank Account        |10105.54                       | 4938.48

Not after parsing json my table look like next:

-------- |-------|Reports_Rows_Rows_Cells_Value|
                 | Business Bank Account       |
                 | 10105.54                    |
                 | 4938.48                     |

My code which I use to parse json

def flatten_df(nested_df):
    # for ncol in nested_df.columns:
    array_cols = [c[0] for c in nested_df.dtypes if c[1][:5] == 'array']
    for col in array_cols:
        nested_df = nested_df.withColumn(col, explode_outer(nested_df[col]))
    nested_cols = [c[0] for c in nested_df.dtypes if c[1][:6] == 'struct']
    if len(nested_cols) == 0:
        return nested_df
    flat_cols = [c[0] for c in nested_df.dtypes if c[1][:6] != 'struct']
    flat_df = nested_df.select(flat_cols + 
                                [F.col(nc+'.'+c).alias(nc+'_'+c) 
                                for nc in nested_cols
                                for c in nested_df.select(nc+'.*').columns])
    return flatten_df(flat_df)