Yes, this would be a violation as the test data for folds 2-10 of the outer cross-validation would have been part of the training data for fold 1 which were used to determine the values of the kernel and regularisation parameters. This means that some information about the test data has potentially leaked into the design of the model, which potentially gives an optimistic bias to the performance evaluation, that is most optimistic for models that are very sensitive to the setting of the hyper-parameters (i.e. it most stongly favours models with an undesirable feature).
This bias is likely to be strongest for small datasets, such as this one, as the variance of the model selection criterion is largest for small datasets, which encourages over-fitting the model selection criterion, which means more information about the test data can leak through.
I wrote a paper on this a year or two ago as I was rather startled by the magnitude of the bias deviations from full nested cross-validation can introduce, which can easily swamp the difference in performance between classifier systems. The paper is "On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation"
Gavin C. Cawley, Nicola L. C. Talbot; JMLR 11(Jul):2079−2107, 2010.
Essentially tuning the hyper-parameters should be considered an integral part of fitting the model, so each time you train the SVM on a new sample of data, independently retune the hyper-parameters for that sample. If you follow that rule, you probably can't go too far wrong. It is well worth the computational expense to get an unbiased performance estimate, as otherwise you run the risk of drawing the wrong conclusions from your experiment.
I think you misunderstand the way folds are generated in cross-validation. In cross-validation, your data set is partitioned at random into a specific number of folds.* The data is not partitioned as you would slice a pie (e.g. adjacent instances belonging to the same fold).
*: in stratified cross-validation the class balance in the overall data set is maintained across folds.
Best Answer
I don't think there is a standard way to choose a range (there are some heuristics for choosing a gamma value). I would look at the libsvm practical tutorial on getting started with SVMs. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
Generally, I don't think you usually see C values that are so low. I would start with 2^-3 to 2^10 for C values and keep the gamma range the same. I would then run a coarse to fine grid search. It looks like you're using steps of 4? I would then search the area around the most accurate parameter set at a smaller step size, like 1.
Hope this helps.