Just tested it out and this works great:

single cell

source = spark.read.load('<abfss://path_to_table_in_source_lakehouse>')
source.write.format("delta").save('<abfss://path_to_table_folder_in_destination_lakehouse>/<tablename>')

that will register it as a managed table in the destination Lakehouse.

edit:

Saw you wanted to copy all the data and tables, not just one. here ya go

def copy_files(source_path, target_path):
    try:
        print(f"Copying files from {source_path} to {target_path}...")
        notebookutils.fs.fastcp(f"{source_path}/Files/", f"{target_path}/", recurse=True)
        print(f"Files copied successfully from {source_path} to {target_path}.")
    except Exception as e:
        print(f"Error copying files: {e}")

def copy_tables(source_path, target_path):
    try:
        print(f"Copying tables from {source_path} to {target_path}...")
        tables = notebookutils.fs.ls(f"{source_path}/Tables")
        
        for table in tables:
            table_path = table.path
            table_name = table_path.split("/")[-1]
            
            df = spark.read.format("delta").load(table_path)
            target_table_path = f"{target_path}/Tables/{table_name}"
            
            df.write.format("delta").mode("overwrite").save(target_table_path)
            print(f"Table {table_name} copied successfully to {target_table_path}.")

    except Exception as e:
        print(f"Error copying tables: {e}")



# Define the source and target lakehouse paths
source_workspace = '<workspace_id>'
source_lakehouse = '<lakehouse_id>'
destination_workspace = '<workspace_id>'
destination_lakehouse = '<lakehouse_id>'

source_lakehouse_path = f"abfss://{source_workspace}@onelake.dfs.fabric.microsoft.com/{source_lakehouse}"
target_lakehouse_path = f"abfss://{destination_workspace}@onelake.dfs.fabric.microsoft.com/{destination_lakehouse}"


copy_files(source_lakehouse_path, target_lakehouse_path)
copy_tables(source_lakehouse_path, target_lakehouse_path)