## Step 1: Define the symbolic representation of the variables
The variables are 'grams of fiber', 'milligrams of potassium', and 'milligrams of magnesium', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $7x_1 + 8x_2 + 1x_3$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
1. $4x_1 + 4x_2 + 15x_3 \geq 24$ (The total combined digestive support index from milligrams of potassium plus milligrams of magnesium has to be greater than or equal to 24 is incorrect, it should be from 'milligrams of potassium' and 'milligrams of magnesium' which is $4x_2 + 15x_3$ and from 'grams of fiber' is $8x_1$),
2. $8x_1 + 15x_3 \geq 25$,
3. $8x_1 + 4x_2 \geq 14$,
4. $8x_1 + 4x_2 + 15x_3 \geq 14$,
5. $-3x_2 + 2x_3 \geq 0$,
6. $-9x_1 + 6x_2 \geq 0$.

Correcting and properly interpreting the constraints:
- The total digestive support index from 'milligrams of potassium' and 'milligrams of magnesium' is $4x_2 + 15x_3 \geq 24$,
- The total combined digestive support index from 'grams of fiber' and 'milligrams of magnesium' is $8x_1 + 15x_3 \geq 25$,
- The total combined digestive support index from 'grams of fiber' and 'milligrams of potassium' is $8x_1 + 4x_2 \geq 14$,
- The total from all is $8x_1 + 4x_2 + 15x_3 \geq 14$, which is less restrictive than the others and can be ignored,
- $-3x_2 + 2x_3 \geq 0$,
- $-9x_1 + 6x_2 \geq 0$.

## 4: Provide the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'grams of fiber'), ('x2', 'milligrams of potassium'), ('x3', 'milligrams of magnesium')],
'objective_function': '7*x1 + 8*x2 + 1*x3',
'constraints': [
    '4*x2 + 15*x3 >= 24',
    '8*x1 + 15*x3 >= 25',
    '8*x1 + 4*x2 >= 14',
    '-3*x2 + 2*x3 >= 0',
    '-9*x1 + 6*x2 >= 0'
]
}
```

## 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()
    
    # Define variables
    x1 = model.addVar(name="grams_of_fiber", lb=0)  # You may use a fractional amount
    x2 = model.addVar(name="milligrams_of_potassium", lb=0)  # The quantity does not have to be a whole number
    x3 = model.addVar(name="milligrams_of_magnesium", lb=0)  # You may have a non-integer amount

    # Objective function
    model.setObjective(7 * x1 + 8 * x2 + x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(4 * x2 + 15 * x3 >= 24)
    model.addConstr(8 * x1 + 15 * x3 >= 25)
    model.addConstr(8 * x1 + 4 * x2 >= 14)
    model.addConstr(-3 * x2 + 2 * x3 >= 0)
    model.addConstr(-9 * x1 + 6 * x2 >= 0)

    # Solve the model
    model.optimize()

    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Grams of fiber: {x1.varValue}")
        print(f"Milligrams of potassium: {x2.varValue}")
        print(f"Milligrams of magnesium: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    elif model.status == gurobi.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print("The model has a non-optimal status.")

solve_optimization_problem()
```