Yes you can add Dense layer with Embedding Layer
Working sample code

import tensorflow as tf
import numpy as np
model = tf.keras.Sequential()
model.add(tf.keras.layers.Embedding(1000, 64, input_length=10))
model.add(tf.keras.layers.Dense(64, activation=‘relu’))
model.add(tf.keras.layers.Dense(1))
input_array = np.random.randint(1000, size=(32, 10))
model.compile(‘rmsprop’, ‘mse’)
output_array = model.predict(input_array)
print(output_array.shape)

Output

(32, 10, 1)