but are there? suppose you take a sample of heights, and then fit a continuous distribution to those heights. When you calculate the probability of sampling a height between two data values, you're really making a guess based upon the data. Heights among humans may actually vary in discrete increments, but these increments may be so tiny (maybe on the order of nanometers) that it's reasonable to just come up with a rule (a distribution) that produces a value for any interval of rational numbers you feed it, even if it's actually invalid for small intervals (on the order of a nanometer, perhaps).
Is there a need for a notion of real numbers here? Your rule assigns a value to any rational number you give it, and there are as many of those as you'd like, but there's a discrete scale at which the distribution is effective anyway. In real statistical applications you normally need to numerically integrate anyway, which requires discretizing your continuous distribution.