To address the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints in terms of these variables.

Let's define:
- $x_1$ as the quantity of grams of fiber,
- $x_2$ as the quantity of milligrams of vitamin B5.

The objective function to minimize is: $1 \cdot x_1 + 2 \cdot x_2$

Given constraints are:
1. Cognitive performance index from grams of fiber: $4x_1$
2. Kidney support index from grams of fiber: $3x_1$
3. Digestive support index from grams of fiber: $x_1$
4. Cognitive performance index from milligrams of vitamin B5: $4x_2$
5. Kidney support index from milligrams of vitamin B5: $4x_2$
6. Digestive support index from milligrams of vitamin B5: $3x_2$

Combined constraints:
- Total cognitive performance index: $4x_1 + 4x_2 \geq 1$ and $4x_1 + 4x_2 = 1$ (which simplifies to just one constraint as they are contradictory in the context of optimization; we'll consider it as $4x_1 + 4x_2 \geq 1$ for feasibility)
- Total kidney support index: $3x_1 + 4x_2 \geq 6$
- Total digestive support index: $x_1 + 3x_2 \geq 6$

Additional constraints:
- $-2x_1 + 4x_2 \geq 0$
- Maximum cognitive performance index: $4x_1 + 4x_2 \leq 11$
- Maximum kidney support index: $3x_1 + 4x_2 \leq 26$
- Maximum digestive support index: $x_1 + 3x_2 \leq 22$

Symbolic representation:
```json
{
    'sym_variables': [('x1', 'grams of fiber'), ('x2', 'milligrams of vitamin B5')],
    'objective_function': '1*x1 + 2*x2',
    'constraints': [
        '4*x1 + 4*x2 >= 1',
        '3*x1 + 4*x2 >= 6',
        'x1 + 3*x2 >= 6',
        '-2*x1 + 4*x2 >= 0',
        '4*x1 + 4*x2 <= 11',
        '3*x1 + 4*x2 <= 26',
        'x1 + 3*x2 <= 22'
    ]
}
```

Now, let's implement this in Gurobi:

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
x1 = m.addVar(name="grams_of_fiber", lb=-GRB.INFINITY)  # Allowed to be non-integer
x2 = m.addVar(name="milligrams_of_vitamin_B5", lb=-GRB.INFINITY)  # Allowed to be non-integer

# Objective function
m.setObjective(1*x1 + 2*x2, GRB.MINIMIZE)

# Constraints
m.addConstr(4*x1 + 4*x2 >= 1, name="cognitive_performance_index_min")
m.addConstr(3*x1 + 4*x2 >= 6, name="kidney_support_index_min")
m.addConstr(x1 + 3*x2 >= 6, name="digestive_support_index_min")
m.addConstr(-2*x1 + 4*x2 >= 0, name="additional_constraint_1")
m.addConstr(4*x1 + 4*x2 <= 11, name="cognitive_performance_index_max")
m.addConstr(3*x1 + 4*x2 <= 26, name="kidney_support_index_max")
m.addConstr(x1 + 3*x2 <= 22, name="digestive_support_index_max")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"grams_of_fiber: {x1.x}")
    print(f"milligrams_of_vitamin_B5: {x2.x}")
else:
    print("No optimal solution found")
```