## Step 1: Define the optimization problem
The problem is to maximize the objective function $6.13L + 3.02D$, where $L$ represents the hours worked by Laura and $D$ represents the hours worked by Dale.

## 2: List all given constraints
- Laura's productivity rating: $4.87L$
- Laura's organization score: $0.28L$
- Laura's paperwork competence rating: $6.33L$
- Laura's computer competence rating: $5.0L$
- Dale's productivity rating: $1.72D$
- Dale's organization score: $6.84D$
- Dale's paperwork competence rating: $4.33D$
- Dale's computer competence rating: $3.52D$

## 3: Translate given attributes into constraints
Given attributes:
- $r0$: productivity rating, upper bound = 119, $x0 = 4.87$, $x1 = 1.72$
- $r1$: organization score, upper bound = 60, $x0 = 0.28$, $x1 = 6.84$
- $r2$: paperwork competence rating, upper bound = 50, $x0 = 6.33$, $x1 = 4.33$
- $r3$: computer competence rating, upper bound = 299, $x0 = 5.0$, $x1 = 3.52$

## 4: Formulate constraints based on problem description
1. $4.87L + 1.72D \geq 41$
2. $0.28L + 6.84D \geq 18$
3. $6.33L + 4.33D \geq 17$
4. $5.0L + 3.52D \geq 110$
5. $-L + 6D \geq 0$
6. $4.87L + 1.72D \leq 92$
7. $0.28L + 6.84D \leq 58$
8. $6.33L + 4.33D \leq 37$
9. $5.0L + 3.52D \leq 231$

## 5: Write Gurobi code for the optimization problem
```python
import gurobi

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

    # Define variables
    L = model.addVar(name="hours_worked_by_Laura", lb=0)
    D = model.addVar(name="hours_worked_by_Dale", lb=0)

    # Objective function: Maximize 6.13L + 3.02D
    model.setObjective(6.13*L + 3.02*D, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(4.87*L + 1.72*D >= 41, name="productivity_rating_constraint")
    model.addConstr(0.28*L + 6.84*D >= 18, name="organization_score_constraint")
    model.addConstr(6.33*L + 4.33*D >= 17, name="paperwork_competence_rating_constraint")
    model.addConstr(5.0*L + 3.52*D >= 110, name="computer_competence_rating_constraint")
    model.addConstr(-L + 6*D >= 0, name="hours_worked_constraint")
    model.addConstr(4.87*L + 1.72*D <= 92, name="productivity_rating_upper_bound")
    model.addConstr(0.28*L + 6.84*D <= 58, name="organization_score_upper_bound")
    model.addConstr(6.33*L + 4.33*D <= 37, name="paperwork_competence_rating_upper_bound")
    model.addConstr(5.0*L + 3.52*D <= 231, name="computer_competence_rating_upper_bound")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Hours worked by Laura: {L.varValue}")
        print(f"Hours worked by Dale: {D.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```