If you look into TF source code you will find
def __init__(self, learning_rate=0.001, rho=0.95, epsilon=1e-8,
"""Construct a new Adadelta optimizer.
learning_rate: A `Tensor` or a floating point value. The learning rate.
To match the exact form in the original paper use 1.0.
rho: A `Tensor` or a floating point value. The decay rate.
epsilon: A `Tensor` or a floating point value. A constant epsilon used
to better conditioning the grad update.
use_locking: If `True` use locks for update operations.
name: Optional name prefix for the operations created when applying
gradients. Defaults to "Adadelta".
Developers are aware that the paper used
They put that notice there due to this issue.
With the exception of SGD, all other major optimizers have
learning_rate=0.001, so it probably got the same value by coincidence.