Adapting ZFNet on 2244x224 image using a filter 7X7
As per the formula for the feature map dimension:
$$feature_{dim} = \frac{n+2p-f}{S} + 1$$
The values for :

n = 224
p = 0
f = 7
s = 3

$$feature_{dim} = \frac{224+2*0-7}{3} + 1 = 73.66 $$
As you've guessed this is not the size of the feature map.
Tensorflow takes it as 73.
If you're relying on the formula, you are missing out on a concept, that this should a process were in the kernel slides over the Input hence the feature map dimension should be an integer. So what happens is that the kernel while sliding with a stride of 3 leaves out the last few pixels and won't reach the other edge.
If you're trying to get a feature map that's an integer by keeping a constant filter size of 7. Then you're stride is :
$$S = 217 /(N - 1)$$
where N is you're desired output size.
If you choose N to be 8 or 32, you'll end up with a stride of 7 or 31. It's better to choose S = 7 to get most of the information. But still, it doesn't matter as TensorFlow has checks for the same to prevent errors.