To solve this optimization problem, we first need to translate the given natural language description into a symbolic representation. This involves defining the variables, the objective function, and the constraints using mathematical notation.

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

The objective function to maximize is given as:
\[7.92 \cdot x_1^2 + 4.18 \cdot x_1 + 2.24 \cdot x_2\]

Now, let's list out the constraints based on the given information:

1. The digestive support index of milligrams of vitamin A is \(2x_1\).
2. The immune support index of milligrams of vitamin A is \(7x_1\).
3. The energy stability index for milligrams of vitamin A is \(2x_1\).
4. The digestive support index of grams of fiber is \(8x_2\).
5. The immune support index of grams of fiber is \(4x_2\).
6. The energy stability index for grams of fiber is \(x_2\).

And the constraints are:
- Total combined digestive support index: \(2x_1^2 + 8x_2^2 \geq 53\) and \(2x_1^2 + 8x_2^2 \leq 80\),
- Total combined immune support index: \(7x_1 + 4x_2 \geq 16\) and \(7x_1 + 4x_2 \leq 46\),
- Total combined energy stability index: \(2x_1 + x_2 \geq 72\) and \(2x_1 + x_2 \leq 85\),
- Additional constraint: \(7x_1 - 3x_2 \geq 0\).

Given these definitions, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin A'), ('x2', 'grams of fiber')],
    'objective_function': '7.92*x1**2 + 4.18*x1 + 2.24*x2',
    'constraints': [
        '2*x1**2 + 8*x2**2 >= 53',
        '2*x1**2 + 8*x2**2 <= 80',
        '7*x1 + 4*x2 >= 16',
        '7*x1 + 4*x2 <= 46',
        '2*x1 + x2 >= 72',
        '2*x1 + x2 <= 85',
        '7*x1 - 3*x2 >= 0'
    ]
}
```

Now, let's implement this problem in Gurobi Python to find the optimal solution:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_A")
x2 = m.addVar(lb=-GRB.INFINITY, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="grams_of_fiber")

# Define the objective function
m.setObjective(7.92*x1**2 + 4.18*x1 + 2.24*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(2*x1**2 + 8*x2**2 >= 53, "digestive_support_index_min")
m.addConstr(2*x1**2 + 8*x2**2 <= 80, "digestive_support_index_max")
m.addConstr(7*x1 + 4*x2 >= 16, "immune_support_index_min")
m.addConstr(7*x1 + 4*x2 <= 46, "immune_support_index_max")
m.addConstr(2*x1 + x2 >= 72, "energy_stability_index_min")
m.addConstr(2*x1 + x2 <= 85, "energy_stability_index_max")
m.addConstr(7*x1 - 3*x2 >= 0, "additional_constraint")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin A: {x1.x}")
    print(f"Grams of Fiber: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```