## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'grams of fiber' and 'milligrams of vitamin B6'. Let's denote 'grams of fiber' as $x_1$ and 'milligrams of vitamin B6' as $x_2$. The objective function to minimize is $3.28x_1 + 1.26x_2$.

## 2: List the constraints in symbolic notation
The constraints given are:
1. $19x_1 + 9x_2 \geq 34$
2. $x_1 + x_2 \geq 59$
3. $9x_1 - x_2 \geq 0$
4. $19x_1 + 9x_2 \leq 118$
5. $x_1 + x_2 \leq 123$
6. $x_1$ must be an integer (or whole number)
7. $x_2$ can be any real number

## 3: Convert the problem into a Gurobi-compatible format
To solve this problem using Gurobi, we need to define the model, add the variables, the objective function, and the constraints.

## 4: Write the Gurobi code
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="grams_of_fiber", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="milligrams_of_vitamin_B6")

    # Define the objective function
    model.setObjective(3.28 * x1 + 1.26 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(19 * x1 + 9 * x2 >= 34, name="digestive_support_index_min")
    model.addConstr(x1 + x2 >= 59, name="immune_support_index_min")
    model.addConstr(9 * x1 - x2 >= 0, name="fiber_vitamin_B6_balance")
    model.addConstr(19 * x1 + 9 * x2 <= 118, name="digestive_support_index_max")
    model.addConstr(x1 + x2 <= 123, name="immune_support_index_max")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Grams of fiber: {x1.varValue}")
        print(f"Milligrams of vitamin B6: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```

## 5: Symbolic representation of the problem
```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',
        'x1 is an integer'
    ]
}
```