My hypothesis is that you found a configuration (learning rate, batch size, number of hidden nodes, etc.) which happened to be better for the sigmoid network than the Leaky ReLU network. I assume that there's an alternative configuration for which the Leaky ReLU network is better than the sigmoid network.
As an aside, the main motivation of ReLU-type activations is that they work better in deep networks, where sigmoid and tanh networks tend to get saturated and the gradient vanishes. Using a network with 1 hidden layer is not necessarily going to highlight the contrast between sigmoid and ReLU activations.
I would caution against drawing any general conclusions from a single experiment using the Iris data. It's a small data set where one of the classes is linearly separable from the rest, so it's only useful as a toy problem; it's just not very complex.