To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each of the items mentioned (grams of fiber and milligrams of vitamin B6), expressing the objective function using these variables, and listing out all constraints in terms of these variables.

Let's denote:
- \(x_1\) as the grams of fiber,
- \(x_2\) as the milligrams of vitamin B6.

The objective function to minimize is: \(3.28x_1 + 1.26x_2\).

Constraints are as follows:
1. Total combined digestive support index from grams of fiber and milligrams of vitamin B6 must be at least 34: \(19x_1 + 9x_2 \geq 34\).
2. Total combined immune support index from grams of fiber and milligrams of vitamin B6 must be at least 59: \(x_1 + x_2 \geq 59\).
3. The constraint \(9x_1 - x_2 \geq 0\) must hold.
4. The total combined digestive support index from grams of fiber and milligrams of vitamin B6 must not exceed 118: \(19x_1 + 9x_2 \leq 118\).
5. The total combined immune support index from grams of fiber and milligrams of vitamin B6 must not exceed 123: \(x_1 + x_2 \leq 123\).
6. Grams of fiber (\(x_1\)) must be a whole number.

Symbolic representation:
```json
{
    'sym_variables': [('x1', 'grams of fiber'), ('x2', 'milligrams of vitamin B6')],
    'objective_function': '3.28*x1 + 1.26*x2',
    'constraints': [
        '19*x1 + 9*x2 >= 34',
        'x1 + x2 >= 59',
        '9*x1 - x2 >= 0',
        '19*x1 + 9*x2 <= 118',
        'x1 + x2 <= 123'
    ]
}
```

Now, let's implement this problem in Gurobi Python:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="grams_of_fiber")  # Whole number constraint for grams of fiber
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B6")  # Continuous variable for milligrams of vitamin B6

# Objective function: Minimize 3.28*x1 + 1.26*x2
m.setObjective(3.28*x1 + 1.26*x2, GRB.MINIMIZE)

# Constraints
m.addConstr(19*x1 + 9*x2 >= 34, name="digestive_support_index_min")
m.addConstr(x1 + x2 >= 59, name="immune_support_index_min")
m.addConstr(9*x1 - x2 >= 0, name="additional_constraint")
m.addConstr(19*x1 + 9*x2 <= 118, name="digestive_support_index_max")
m.addConstr(x1 + x2 <= 123, name="immune_support_index_max")

# Optimize model
m.optimize()

if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Grams of Fiber: {x1.x}")
    print(f"Milligrams of Vitamin B6: {x2.x}")
else:
    print("No optimal solution found")
```