To solve this optimization problem, we need to first define the symbolic representation of the variables and the objective function, as well as the constraints. 

The variables in this problem can be represented symbolically as follows:
- $x_1$ for 'apples'
- $x_2$ for 'eggs'
- $x_3$ for 'bowls of instant ramen'

Given these symbolic representations, we can now formulate the objective function and constraints using these symbols.

The objective function to maximize is: 
\[9.29x_1^2 + 3.05x_1x_2 + 9.45x_2x_3 + 7.93x_1 + 6.27x_2 + 8.08x_3\]

The constraints are as follows:
1. Fiber from apples: $2.67x_1$
2. Fiber from eggs: $0.43x_2$
3. Fiber from bowls of instant ramen: $2.94x_3$
4. At least 31 grams of fiber from eggs and bowls of instant ramen: $0.43x_2 + 2.94x_3 \geq 31$
5. At least 16 grams of fiber from apples squared and bowls of instant ramen squared: $(2.67x_1)^2 + (2.94x_3)^2 \geq 16$
6. No more than 97 grams of fiber from eggs squared plus bowls of instant ramen squared: $(0.43x_2)^2 + (2.94x_3)^2 \leq 97$
7. At most 90 grams of fiber from apples plus eggs: $2.67x_1 + 0.43x_2 \leq 90$
8. At most 90 grams of fiber from apples, eggs, and bowls of instant ramen: $2.67x_1 + 0.43x_2 + 2.94x_3 \leq 90$

Symbolic representation in JSON format:
```json
{
    'sym_variables': [('x1', 'apples'), ('x2', 'eggs'), ('x3', 'bowls of instant ramen')],
    'objective_function': '9.29*x1**2 + 3.05*x1*x2 + 9.45*x2*x3 + 7.93*x1 + 6.27*x2 + 8.08*x3',
    'constraints': [
        '0.43*x2 + 2.94*x3 >= 31',
        '(2.67*x1)**2 + (2.94*x3)**2 >= 16',
        '(0.43*x2)**2 + (2.94*x3)**2 <= 97',
        '2.67*x1 + 0.43*x2 <= 90',
        '2.67*x1 + 0.43*x2 + 2.94*x3 <= 90'
    ]
}
```

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 the variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="apples")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="eggs")
x3 = m.addVar(vtype=GRB.CONTINUOUS, name="bowls_of_instant_ramen")

# Define the objective function
m.setObjective(9.29*x1**2 + 3.05*x1*x2 + 9.45*x2*x3 + 7.93*x1 + 6.27*x2 + 8.08*x3, GRB.MAXIMIZE)

# Define the constraints
m.addConstr(0.43*x2 + 2.94*x3 >= 31, name="fiber_from_eggs_and_ramen")
m.addConstr((2.67*x1)**2 + (2.94*x3)**2 >= 16, name="fiber_from_apples_and_ramen_squared")
m.addConstr((0.43*x2)**2 + (2.94*x3)**2 <= 97, name="fiber_from_eggs_and_ramen_squared")
m.addConstr(2.67*x1 + 0.43*x2 <= 90, name="fiber_from_apples_and_eggs")
m.addConstr(2.67*x1 + 0.43*x2 + 2.94*x3 <= 90, name="fiber_from_all")

# Optimize the model
m.optimize()

# Print the results
for v in m.getVars():
    print(f"{v.varName}: {v.x}")

print(f"Objective value: {m.objVal}")
```