To solve the given optimization problem using Gurobi, we first need to translate the natural language description into a mathematical formulation. The objective function and constraints are described as follows:

- **Objective Function:** Maximize \(9.29 \times (\text{apples})^2 + 3.05 \times (\text{apples}) \times (\text{eggs}) + 9.45 \times (\text{eggs}) \times (\text{bowls of instant ramen}) + 7.93 \times (\text{apples}) + 6.27 \times (\text{eggs}) + 8.08 \times (\text{bowls of instant ramen})\)

- **Constraints:**
  - Fiber from apples, eggs, and bowls of instant ramen does not exceed the upper bound.
  - At least 31 grams of fiber come from eggs plus bowls of instant ramen.
  - At least 16 grams of fiber come from apples squared plus bowls of instant ramen squared.
  - No more than 97 grams of fiber come from eggs squared plus bowls of instant ramen squared.
  - At most 90 grams of fiber can come from apples plus eggs.
  - At most 90 grams of fiber can come from apples, eggs, and bowls of instant ramen.

Given the variables:
- \(x_0 = \text{apples}\)
- \(x_1 = \text{eggs}\)
- \(x_2 = \text{bowls of instant ramen}\)

And the resources/attributes:
- Fiber content per unit: \(2.67\) grams for apples, \(0.43\) grams for eggs, and \(2.94\) grams for bowls of instant ramen.

We need to ensure that all variables are non-negative since negative quantities do not make sense in this context.

Now, let's write the Gurobi code for this problem:

```python
from gurobipy import *

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

# Define variables
apples = m.addVar(vtype=GRB.CONTINUOUS, name="apples", lb=0)
eggs = m.addVar(vtype=GRB.CONTINUOUS, name="eggs", lb=0)
bowls_of_instant_ramens = m.addVar(vtype=GRB.CONTINUOUS, name="bowls_of_instant_ramens", lb=0)

# Define the objective function
m.setObjective(9.29 * apples**2 + 3.05 * apples * eggs + 9.45 * eggs * bowls_of_instant_ramens + 
               7.93 * apples + 6.27 * eggs + 8.08 * bowls_of_instant_ramens, GRB.MAXIMIZE)

# Define constraints
m.addConstr(2.67 * apples + 0.43 * eggs + 2.94 * bowls_of_instant_ramens <= 116, name="Total_Fiber")
m.addConstr(0.43 * eggs + 2.94 * bowls_of_instant_ramens >= 31, name="Eggs_and_Ramens_Minimum")
m.addConstr((apples**2) * 2.67 + (bowls_of_instant_ramens**2) * 2.94 >= 16, name="Apples_and_Ramens_Squared_Minimum")
m.addConstr((eggs**2) * 0.43 + (bowls_of_instant_ramens**2) * 2.94 <= 97, name="Eggs_and_Ramens_Squared_Maximum")
m.addConstr(2.67 * apples + 0.43 * eggs <= 90, name="Apples_and_Eggs_Fiber_Maximum")
m.addConstr(2.67 * apples + 0.43 * eggs + 2.94 * bowls_of_instant_ramens <= 90, name="All_Items_Fiber_Maximum")

# Optimize the model
m.optimize()

# Print results
for v in m.getVars():
    print(f"{v.varName}: {v.x}")
print("Objective:", m.objVal)
```