All TF-IDF weighting schemes are just heuristic methods to give more weight to unusual terms. I'm not sure that TF-IDF schemes generally have a solid statistical basis behind them (see reference 1), except for the observation that TF-IDF tends to produce better results than simple word counts. Since the quality of the results is the primary (sole?) justification for TF-IDF in the first place, one could argue that trying your method with and without +1 and picking the best one would be fine.
If I'm reading this sckit learn thread correctly, it appears that you are not the first person to raise a similar question about adding 1 to IDF scores. The consensus on that thread is that +1 is nonstandard behavior as well. I only skimmed it, but the thread does not appear contain a resounding endorsement or justification of +1.
So the choice of +1 has the effect of placing the lower bound on all IDF values at 1 rather than at 0. This is the same as adding $e$ documents containing every word to your corpus. Not sure why that might be helpful, but perhaps it is in specific contexts. One might even treat some parameter $c$ in $c+\log\left(\frac{\text{numDocs}}{\text{docFreq+1}}\right)$ as a tuning parameter, to give you a more flexible family of IDF schemes with $c$ as their lower bound.
When the lower bound of IDF is zero, the product $\text{term frequency}\times\text{IDF}$ may be 0 for some terms, so that those terms are given no weight at all in the learning procedure; qualitatively, the terms are so common that they provide no information relevant to the NLP task. When the lower-bound is nonzero, these terms will have more influence.
- John Lafferty and Guy Lebanon. "Diffusion Kernels on Statistical Manifolds." Journal of Machine Learning. 2005.
