To solve this optimization problem, we first need to understand and translate the given natural language description into a symbolic representation. The variables in this problem are:

- `x1`: milligrams of vitamin C
- `x2`: grams of fiber
- `x3`: milligrams of vitamin B3

The objective function to minimize is: `6*x1 + 6*x2 + 2*x3`

Given the constraints, we have:
1. Cognitive performance index for milligrams of vitamin C: `4*x1`
2. Cognitive performance index for grams of fiber: `5*x2`
3. Cognitive performance index for milligrams of vitamin B3: `1*x3`
4. Total combined cognitive performance index from milligrams of vitamin C and grams of fiber must be at least 14: `4*x1 + 5*x2 >= 14`
5. Total combined cognitive performance index from grams of fiber and milligrams of vitamin B3 must be at least 7: `5*x2 + 1*x3 >= 7`
6. Total combined cognitive performance index from milligrams of vitamin C, grams of fiber, and milligrams of vitamin B3 must be at least 17: `4*x1 + 5*x2 + 1*x3 >= 17`
7. Another constraint reiterating the total combined cognitive performance index from all three must be at least 17 (seems to be a repetition): `4*x1 + 5*x2 + 1*x3 >= 17`
8. `-7*x2 + 7*x3 >= 0`
9. `-5*x1 + x3 >= 0`
10. `-x1 + 10*x2 >= 0`
11. Total combined cognitive performance index from grams of fiber and milligrams of vitamin B3 must be at most 26: `5*x2 + 1*x3 <= 26`

The symbolic representation is:
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin C'), ('x2', 'grams of fiber'), ('x3', 'milligrams of vitamin B3')],
    'objective_function': '6*x1 + 6*x2 + 2*x3',
    'constraints': [
        '4*x1 >= 0', 
        '5*x2 >= 0', 
        '1*x3 >= 0', 
        '4*x1 + 5*x2 >= 14', 
        '5*x2 + 1*x3 >= 7', 
        '4*x1 + 5*x2 + 1*x3 >= 17',
        '-7*x2 + 7*x3 >= 0',
        '-5*x1 + x3 >= 0',
        '-x1 + 10*x2 >= 0',
        '5*x2 + 1*x3 <= 26'
    ]
}
```

To implement this in Gurobi, we'll write the Python code as follows:

```python
from gurobipy import *

# Create a new model
model = Model("Vitamin_Optimization")

# Create variables
x1 = model.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_C")
x2 = model.addVar(vtype=GRB.CONTINUOUS, name="grams_of_fiber")
x3 = model.addVar(vtype=GRB.INTEGER, name="milligrams_of_vitamin_B3")

# Set the objective function
model.setObjective(6*x1 + 6*x2 + 2*x3, GRB.MINIMIZE)

# Add constraints
model.addConstr(4*x1 + 5*x2 >= 14, "cognitive_performance_index_1")
model.addConstr(5*x2 + 1*x3 >= 7, "cognitive_performance_index_2")
model.addConstr(4*x1 + 5*x2 + 1*x3 >= 17, "total_cognitive_performance_index")
model.addConstr(-7*x2 + 7*x3 >= 0, "constraint_8")
model.addConstr(-5*x1 + x3 >= 0, "constraint_9")
model.addConstr(-x1 + 10*x2 >= 0, "constraint_10")
model.addConstr(5*x2 + 1*x3 <= 26, "max_cognitive_performance_index")

# Optimize model
model.optimize()

# Print solution
if model.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin C: {x1.x}")
    print(f"Grams of Fiber: {x2.x}")
    print(f"Milligrams of Vitamin B3: {x3.x}")
else:
    print("No optimal solution found")
```