Understanding of number of cells in layers of sequential models
RNN cell versus neuron
You are hesitating between RNN cells and neurons. I understand you refer to RNN cells in your question. And so, traditionally any layer of a sequence model will always have the same number of cells as the length of your sequence or embedding size.
See difference between cell and neuron here: Difference between cell state and hidden state
See input size for sequence models here: How can I picture an unfolded RNN as a normal Feed Forward Network?
Re; code snippet
In SimpleRNN, the first argument is the number of units in each cell i.e. number of neurons in each cell. The number of cells is not an argument in sequential layers, and always is equal to the length of your input sequences or size of embedding layer.
model = Sequential()
model.add(Embedding(maxWordsCount, 256, input_length = inp_words))
# Below, 128 is the neurons of each cell, and relates to the cell memory capacity.
model.add(SimpleRNN(128, activation='tanh'))