To solve the given optimization problem using Gurobi, we need to translate the natural language description into a mathematical formulation that can be expressed in code. The goal is to maximize the objective function subject to several constraints.

The objective function to maximize is: $8.61 \times (\text{hours worked by Jean})^2 + 8.54 \times (\text{hours worked by Jean}) \times (\text{hours worked by George}) + 1.04 \times (\text{hours worked by George})^2 + 3.32 \times (\text{hours worked by Jean}) + 8.36 \times (\text{hours worked by George})$.

The constraints are:
1. The work quality rating for Jean is fixed at 17.
2. The work quality rating for George is fixed at 6.
3. The total combined work quality rating from hours worked by Jean squared and hours worked by George squared must be at least 49.
4. $3 \times (\text{hours worked by Jean})^2 - 9 \times (\text{hours worked by George})^2 \geq 0$.
5. The total combined work quality rating from hours worked by Jean plus hours worked by George should not exceed 152.
6. The constraint on the total combined work quality rating is an equality for maximization purposes, but since we are given it as a "should be 152 or less" and then immediately as "must be 152 at maximum," we interpret this as needing to be exactly 152 for our optimization problem setup.
7. Hours worked by Jean must be an integer.
8. Hours worked by George must be an integer.

Given these constraints, the Gurobi code can be formulated as follows:

```python
from gurobipy import *

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

# Define variables
hours_worked_by_jean = m.addVar(vtype=GRB.INTEGER, name="hours_worked_by_jean")
hours_worked_by_george = m.addVar(vtype=GRB.INTEGER, name="hours_worked_by_george")

# Objective function: maximize
m.setObjective(8.61 * hours_worked_by_jean**2 + 8.54 * hours_worked_by_jean * hours_worked_by_george + 
               1.04 * hours_worked_by_george**2 + 3.32 * hours_worked_by_jean + 8.36 * hours_worked_by_george, GRB.MAXIMIZE)

# Constraints
m.addConstr(17 * hours_worked_by_jean == 17 * hours_worked_by_jean)  # This constraint is always true and does not affect the model
m.addConstr(6 * hours_worked_by_george == 6 * hours_worked_by_george)  # Similarly, this constraint is redundant
m.addConstr(hours_worked_by_jean**2 + hours_worked_by_george**2 >= 49)
m.addConstr(3 * hours_worked_by_jean**2 - 9 * hours_worked_by_george**2 >= 0)
m.addConstr(17 * hours_worked_by_jean + 6 * hours_worked_by_george == 152)  # Combined work quality rating constraint

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hours worked by Jean: {hours_worked_by_jean.x}")
    print(f"Hours worked by George: {hours_worked_by_george.x}")
else:
    print("No optimal solution found")
```