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A few possible hints:

• without Sage: g(x) vanishes if, and only if, x^2 or cos(2*x) vanish, if, and only if, x=0 or there exists k in ZZ such that x = pi/4 + k*pi/2.

• now, plot the graph of g around zero:

  sage: g.plot()
  

• the root at zero is "double" (aroind zero, g is very similar to x^2), so that a small perturbation (due to numerical noise) of g could either lead to two (roughly opposite) zeros, or no zero at all.

• that said, 7.755077210568017e-09 is pretty huge for a numerical noise, how could that be ?

• note that 0.1 is not an exact floating-point number, as it can not be written with a finite binary expansion exactly, hence the addition of the step 0.1 with itself could lead to some accumulated shift, see the middle of the output of:

  sage: for num in [-10..10,step=.1]: 
  ....:     print(num)
  [...]
  -0.200000000000019
  -0.100000000000019
  -1.87905246917808e-14
  0.0999999999999812
  0.199999999999981
  0.299999999999981
  [...]
  

• compare

  sage: find_root(g,-0.100000000000019,-1.87905246917808e-14)                                                                                                                                                  
  -7.755114791616871e-09
  

  with

  sage: find_root(g,-0.1,0)                                                                                                                                                                                    
  0.0
  

A few possible hints:

• without Sage: g(x) vanishes if, and only if, x^2 or cos(2*x) vanish, if, and only if, x=0 or there exists k in ZZ such that x = pi/4 + k*pi/2.

• now, plot the graph of g around zero:

  sage: g.plot()
  

• the root at zero is "double" (aroind (around zero, g is very similar to x^2), so that a small perturbation (due to numerical noise) of g could either lead to two (roughly opposite) zeros, or no zero at all.

• that said, 7.755077210568017e-09 is pretty huge for a numerical noise, how could that be ?

• note that 0.1 is not an exact floating-point number, as it can not be written with a finite binary expansion exactly, hence the addition of the step 0.1 with itself could lead to some accumulated shift, see the middle of the output of:

  sage: for num in [-10..10,step=.1]: 
  ....:     print(num)
  [...]
  -0.200000000000019
  -0.100000000000019
  -1.87905246917808e-14
  0.0999999999999812
  0.199999999999981
  0.299999999999981
  [...]
  

• compare

  sage: find_root(g,-0.100000000000019,-1.87905246917808e-14)                                                                                                                                                  
  -7.755114791616871e-09
  

  with

  sage: find_root(g,-0.1,0)                                                                                                                                                                                    
  0.0