## Problem Description and Formulation

The problem is an optimization problem where we need to maximize the objective function:

\[ 6.54 \times \text{hours worked by Hank} + 6.17 \times \text{hours worked by Dale} \]

subject to several constraints.

### Constraints

1. **Individual Attributes**:
   - Hank's likelihood to quit index: 18
   - Hank's work quality rating: 8
   - Dale's likelihood to quit index: 7
   - Dale's work quality rating: 16

2. **Combined Constraints**:
   - Total combined likelihood to quit index \(\geq 57\)
   - Total combined work quality rating \(\geq 67\)
   - \(4 \times \text{hours worked by Hank} - \text{hours worked by Dale} \geq 0\)
   - Total combined likelihood to quit index \(\leq 123\)
   - Total combined work quality rating \(\leq 70\)

3. **Variable Constraints**:
   - Hours worked by Hank must be an integer.
   - Hours worked by Dale can be any real number.

### Gurobi Code Formulation

```python
import gurobi

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

    # Define variables
    # Assuming x0 is hours worked by Hank and x1 is hours worked by Dale
    x0 = model.addVar(name="hours_worked_by_Hank", vtype=gurobi.GRB.INTEGER)
    x1 = model.addVar(name="hours_worked_by_Dale")

    # Objective function
    model.setObjective(6.54 * x0 + 6.17 * x1, gurobi.GRB.MAXIMIZE)

    # Constraints
    # Individual attributes are implicitly handled as they are constants
    # Combined constraints
    model.addConstr(18 * x0 + 7 * x1 >= 57, name="likelihood_to_quit_min")
    model.addConstr(8 * x0 + 16 * x1 >= 67, name="work_quality_rating_min")
    model.addConstr(4 * x0 - x1 >= 0, name="hank_dale_work_ratio")
    model.addConstr(18 * x0 + 7 * x1 <= 123, name="likelihood_to_quit_max")
    model.addConstr(8 * x0 + 16 * x1 <= 70, name="work_quality_rating_max")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Hours worked by Hank: {x0.varValue}")
        print(f"Hours worked by Dale: {x1.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("The model is infeasible.")

solve_optimization_problem()
```