So the dropout in the last layer would be operating on what is the output of the RNN.
This means you can do it yourself on the output if needed, an option you don’t have for the inner layers.
Note that the dropout implemented by the RNN is not the dropout using one random draw for all timesteps.
Best regards
Thomas