## Step 1: Define the symbolic representation of the variables
The variables are 'sashimi' and 'hamburgers', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $6.76x_1^2 + 4.69x_1x_2 + 2.99x_2^2$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $8x_1 + 12x_2 \geq 41$ (total combined sourness index)
- $6x_1 + 9x_2 \geq 17$ (total combined grams of fiber)
- $-5x_1 + 4x_2 \geq 0$ (linear combination of sashimi and hamburgers)
- $8^2x_1^2 + 12^2x_2^2 \leq 80$ (total combined sourness index squared)
- $6x_1 + 9x_2 \leq 61$ (total combined grams of fiber upper bound)
- $x_1, x_2 \geq 0$ (non-negativity, though not explicitly stated, implied for quantities of food)

However, upon closer inspection, we see that the constraint $8^2x_1^2 + 12^2x_2^2 \leq 80$ simplifies to $64x_1^2 + 144x_2^2 \leq 80$.

## 4: Correct and Simplify Constraints
Correcting and simplifying the constraints:
- The sourness index constraint is $8x_1 + 12x_2 \geq 41$.
- The fiber constraint is $6x_1 + 9x_2 \geq 17$.
- The linear combination constraint is $-5x_1 + 4x_2 \geq 0$.
- The squared sourness index constraint is $64x_1^2 + 144x_2^2 \leq 80$.
- The upper bound on fiber is $6x_1 + 9x_2 \leq 61$.

## 5: Symbolic Representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'sashimi'), ('x2', 'hamburgers')],
'objective_function': '6.76*x1^2 + 4.69*x1*x2 + 2.99*x2^2',
'constraints': [
    '8*x1 + 12*x2 >= 41',
    '6*x1 + 9*x2 >= 17',
    '-5*x1 + 4*x2 >= 0',
    '64*x1^2 + 144*x2^2 <= 80',
    '6*x1 + 9*x2 <= 61'
]
}
```

## 6: Gurobi Code
Now, let's write the Gurobi code to solve this problem:
```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="sashimi", lb=0)  # Assuming non-negativity
    x2 = model.addVar(name="hamburgers", lb=0)  # Assuming non-negativity

    # Objective function
    model.setObjective(6.76 * x1**2 + 4.69 * x1 * x2 + 2.99 * x2**2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(8 * x1 + 12 * x2 >= 41, name="sourness_index")
    model.addConstr(6 * x1 + 9 * x2 >= 17, name="fiber_content")
    model.addConstr(-5 * x1 + 4 * x2 >= 0, name="linear_combination")
    model.addConstr(64 * x1**2 + 144 * x2**2 <= 80, name="squared_sourness_index")
    model.addConstr(6 * x1 + 9 * x2 <= 61, name="fiber_upper_bound")

    # Optimize
    model.optimize()

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

solve_optimization_problem()
```