The confusion is perhaps arising from the fact that the two examples which you have referenced are plotting results of the fft differently. Please refer to the code below for the references made in this explanation.
In the first example, the plot is of the power spectrum (periodogram) over the frequency range. Note, in the first plot, that the periodogram is not centered at 0, meaning that the frequency range appears to be twice that of the Nyquist sampling frequency. As mentioned in the mathworks link, it is common practice to center the periodogram at 0 to avoid this confusion (figure 2).
For the second example, taking the same parameters, the original plot is of amplitude of the fourier spectrum with a different normalization than in the first example (figure 3). Using the syntax of Matlab's full frequency ordering (as commented in the code), it is trivial to convert this seemingly different fft result to that of example 1; the identical result of the 0-centered periodogram is replicated in figure 4.
Thus, to answer your question specifically, the frequency ranges in both cases are the same, with the maximum frequency equal to the Nyquist sampling frequency as in:
f = fs/2*linspace(0,1,NFFT/2+1);
The key to understanding how the dfft works (also in Matlab) is to understand that you are simply performing a projection of your discrete data set into fourier space where what is returned by the fft() function in matlab are the coefficients of the expansion for each frequency component and the order of the coefficients is given (in Matlab as in example 2) by:
f = [f(1:end-1) -fliplr(f(1,2:end))];
See the Wikipedia page on the DFT for additional details:
https://en.wikipedia.org/wiki/Discrete_Fourier_transform
It might also be helpful for you to take the fft omitting the length as a power of 2 parameter as
y = fft(x).
In this case, you would see only a few non-zero components in y corresponding to the exact coefficients of your input signal. The mathworks page claims the following as a motivation for using or not using this length:
"Using a power of two for the transform length optimizes the FFT algorithm, though in practice there is usually little difference in execution time from using n = m."
%% First example:
% http://www.mathworks.com/help/matlab/math/fast-fourier-transform-fft.html
fs = 10; % Sample frequency (Hz)
t = 0:1/fs:10-1/fs; % 10 sec sample
x = (1.3)*sin(2*pi*15*t) ... % 15 Hz component
+ (1.7)*sin(2*pi*40*(t-2)); % 40 Hz component
% Removed the noise
m = length(x); % Window length
n = pow2(nextpow2(m)); % Transform length
y = fft(x,n); % DFT
f = (0:n-1)*(fs/n); % Frequency range
power = y.*conj(y)/n; % Power of the DFT
subplot(2,2,1)
plot(f,power,'-o')
xlabel('Frequency (Hz)')
ylabel('Power')
title('{\bf Periodogram}')
y0 = fftshift(y); % Rearrange y values
f0 = (-n/2:n/2-1)*(fs/n); % 0-centered frequency range
power0 = y0.*conj(y0)/n; % 0-centered power
subplot(2,2,2)
plot(f0,power0,'-o')
% plot(f0,sqrt_power0,'-o')
xlabel('Frequency (Hz)')
ylabel('Power')
title('{\bf 0-Centered Periodogram} Ex. 1')
%% Second example:
% http://stackoverflow.com/questions/10758315/understanding-matlab-fft-example
% Let's redefine the parameters for consistency between the two examples
Fs = fs; % Sampling frequency
% T = 1/Fs; % Sample time (not required)
L = m; % Length of signal
% t = (0:L-1)*T; % Time vector (as above)
% % Sum of a 3 Hz sinusoid and a 2 Hz sinusoid
% x = 0.7*sin(2*pi*3*t) + sin(2*pi*2*t); %(as above)
NFFT = 2^nextpow2(L); % Next power of 2 from length of y
% NFFT == n (from above)
Y = fft(x,NFFT)/L;
f = Fs/2*linspace(0,1,NFFT/2+1);
% Plot single-sided amplitude spectrum.
subplot(2,2,3)
plot(f,2*abs(Y(1:NFFT/2+1)),'-o')
title('Single-Sided Amplitude Spectrum of y(t)')
xlabel('Frequency (Hz)')
ylabel('|Y(f)|')
% Get the 0-Centered Periodogram using the parameters of the second example
f = [f(1:end-1) -fliplr(f(1,2:end))]; % This is the frequency ordering used
% by the full fft in Matlab
power = (Y*L).*conj(Y*L)/NFFT;
% Rearrange for nicer plot
ToPlot = [f; power]; [~,ind] = sort(f);
ToPlot = ToPlot(:,ind);
subplot(2,2,4)
plot(ToPlot(1,:),ToPlot(2,:),'-o')
xlabel('Frequency (Hz)')
ylabel('Power')
title('{\bf 0-Centered Periodogram} Ex. 2')
