Some conditions on the explanatory variables are required for consistency
As with other regression models, the MLEs in logistic regression are not necessarily consistent --- consistency requires imposing some assumptions on the sequence of explanatory variables used in the limit. In the case of Gaussian linear regression we obtain consistency of the parameter estimator using the Grenander conditions (see related answers here, here and here), which (roughly speaking) requires the maximum leverage of the explanatory variables to converge to zero. It is likely that a similar requirement would be needed to prove consistency for the case of the logistic regression. In general, so long as the maximum leverage converges to zero you get a situation where no individual data point, or finite set of data points, is "influential" in the limit.
In view of this, the real question you need to be asking is: what is a reasonable set of sufficient conditions we can impose on the explanatory variables which yield a consistent MLE for the parameters in the logistic regression model? If you undertake this inquiry and find sufficient conditions of this kind, you will be able to see how consistency is established (usually in combination with asymptotic normality) and you will therefore be able to see why the required conditions are weaker than requiring the response variables in the model to be IID. While there are lots of different sufficient conditions that can be formulated, I recommend starting off by reviewing some standard conditions (see e.g., Fahrmeir and Kaufmann (1985) to get you started).