To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints in terms of these variables.

Given:
- Variables: `sashimi` and `hamburgers`
- Resources/Attributes:
  - `r0`: sourness index with upper bound 86, where `sashimi` contributes 8 units and `hamburgers` contribute 12 units.
  - `r1`: grams of fiber with upper bound 103, where `sashimi` contributes 6 units and `hamburgers` contribute 9 units.

Objective Function:
Minimize \(6.76 \times (\text{sashimi})^2 + 4.69 \times \text{sashimi} \times \text{hamburgers} + 2.99 \times (\text{hamburgers})\)

Constraints:
1. Total sourness index from `sashimi` and `hamburgers` is at least 41.
2. At least 17 grams of fiber come from `sashimi` and `hamburgers`.
3. \(-5 \times \text{sashimi} + 4 \times \text{hamburgers} \geq 0\)
4. Total combined sourness index from `sashimi` squared and `hamburgers` squared is at most 80.
5. No more than 61 grams of fiber come from `sashimi` and `hamburgers`.

Let's denote `sashimi` as \(x_1\) and `hamburgers` as \(x_2\).

Symbolic Representation:
```json
{
  'sym_variables': [('x1', 'sashimi'), ('x2', 'hamburgers')],
  'objective_function': '6.76 * (x1)**2 + 4.69 * x1 * x2 + 2.99 * (x2)',
  'constraints': [
    '8*x1 + 12*x2 >= 41',
    '6*x1 + 9*x2 >= 17',
    '-5*x1 + 4*x2 >= 0',
    '(8*x1)**2 + (12*x2)**2 <= 80',
    '6*x1 + 9*x2 <= 61'
  ]
}
```

Now, let's write the Gurobi code to solve this optimization problem:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="sashimi")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="hamburgers")

# Set the objective function
m.setObjective(6.76 * (x1)**2 + 4.69 * x1 * x2 + 2.99 * (x2), GRB.MINIMIZE)

# Add constraints
m.addConstr(8*x1 + 12*x2 >= 41, name="sourness_index_min")
m.addConstr(6*x1 + 9*x2 >= 17, name="fiber_min")
m.addConstr(-5*x1 + 4*x2 >= 0, name="mix_constraint")
m.addConstr((8*x1)**2 + (12*x2)**2 <= 80, name="sourness_index_max_squared")
m.addConstr(6*x1 + 9*x2 <= 61, name="fiber_max")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"sashimi: {x1.x}, hamburgers: {x2.x}")
else:
    print("No optimal solution found")
```
```python 
```