## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B2', 'milligrams of vitamin B1', 'milligrams of zinc']. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ for 'milligrams of vitamin B2'
- $x_2$ for 'milligrams of vitamin B1'
- $x_3$ for 'milligrams of zinc'

## 3: Express the objective function using symbolic variables
The objective function to maximize is: $1x_1^2 + 3x_1x_2 + 7x_2^2 + 6x_2x_3 + 2x_3^2 + 8x_1 + 8x_2$.

## 4: Define the constraints using symbolic variables
The constraints are:
- $13x_1 \leq 128$ (energy stability index of $x_1$)
- $11x_2 \leq 128$ (energy stability index of $x_2$)
- $4x_3 \leq 128$ (energy stability index of $x_3$)
- $13x_1 + 11x_2 \leq 111$ (total combined energy stability index from $x_1$ and $x_2$)
- $13x_1 + 4x_3 \leq 65$ (total combined energy stability index from $x_1$ squared and $x_3$ squared, assuming the squared terms are considered directly)
- $13x_1 + 11x_2 + 4x_3 \leq 51$ (total combined energy stability index from $x_1$, $x_2$, and $x_3$)
- $x_1 + x_2 + x_3 \leq 51$ (total combined)

However, upon closer inspection, it seems there might have been a misunderstanding in directly translating the constraints, especially regarding the energy stability index and its application. The correct interpretation should directly utilize the given attributes:
- $x_1$ has an energy stability index of 13
- $x_2$ has an energy stability index of 11
- $x_3$ has an energy stability index of 4
- The total energy stability index from $x_1$ and $x_2$ is $13 + 11 = 24x_1 + 0x_2 + 0x_3$ is not correct; instead, we consider $13x_1 + 11x_2 \leq 111$
- For $x_1^2$ and $x_3^2$: $13x_1^2 + 4x_3^2 \leq 65$
- For $x_1$, $x_2$, and $x_3$: $13x_1 + 11x_2 + 4x_3 \leq 51$
- And $x_1 + x_2 + x_3 \leq 51$

## 5: Correctly formulate the problem in symbolic notation
The symbolic representation is:
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B2'), ('x2', 'milligrams of vitamin B1'), ('x3', 'milligrams of zinc')],
    'objective_function': '1*x1^2 + 3*x1*x2 + 7*x2^2 + 6*x2*x3 + 2*x3^2 + 8*x1 + 8*x2',
    'constraints': [
        '13*x1 <= 128',
        '11*x2 <= 128',
        '4*x3 <= 128',
        '13*x1 + 11*x2 <= 111',
        '13*x1^2 + 4*x3^2 <= 65',
        '13*x1 + 11*x2 + 4*x3 <= 51',
        'x1 + x2 + x3 <= 51'
    ]
}
```

## 6: Implement the optimization problem using Gurobi
```python
import gurobi

def optimize_problem():
    model = gurobi.Model()
    
    # Define variables
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x1")
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x2")
    x3 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x3")

    # Objective function
    model.setObjective(1*x1**2 + 3*x1*x2 + 7*x2**2 + 6*x2*x3 + 2*x3**2 + 8*x1 + 8*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(13*x1 <= 128)
    model.addConstr(11*x2 <= 128)
    model.addConstr(4*x3 <= 128)
    model.addConstr(13*x1 + 11*x2 <= 111)
    model.addConstr(13*x1**2 + 4*x3**2 <= 65)
    model.addConstr(13*x1 + 11*x2 + 4*x3 <= 51)
    model.addConstr(x1 + x2 + x3 <= 51)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"x1: {x1.varValue}")
        print(f"x2: {x2.varValue}")
        print(f"x3: {x3.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```