## Step 1: Define the optimization problem and the objective function
The objective function to minimize is $5x_0 + 1x_1 + 5x_2$, where $x_0$ represents the hours worked by Dale, $x_1$ represents the hours worked by Peggy, and $x_2$ represents the hours worked by Laura.

## Step 2: List all the constraints
The constraints given are:
- $7x_0 \leq 7$ (Dale's likelihood to quit index is 7)
- $3x_0 \leq 3$ (Dale's organization score is 3)
- $5x_0 \leq 5$ (Dale's work quality rating is 5)
- $4x_0 \leq 4$ (Dale's computer competence rating is 4)
- $3x_1 \leq 3$ (Peggy's likelihood to quit index is 3)
- $6x_1 \leq 6$ (Peggy's organization score is 6)
- $5x_1 \leq 5$ (Peggy's work quality rating is 5)
- $3x_1 \leq 3$ (Peggy's computer competence rating is 3)
- $2x_2 \leq 2$ (Laura's likelihood to quit index is 2)
- $7x_2 \leq 7$ (Laura's organization score is 7)
- $8x_2 \leq 8$ (Laura's work quality rating is 8)
- $5x_2 \leq 5$ (Laura's computer competence rating is 5)
- $3x_1 + 2x_2 \geq 8$ (Total combined likelihood to quit index from Peggy and Laura)
- $7x_0 + 3x_1 + 2x_2 \geq 8$ (Total combined likelihood to quit index from all)
- $3x_0 + 7x_2 \geq 34$ (Total combined organization score from Dale and Laura)
- $3x_0 + 6x_1 + 7x_2 \geq 33$ (Total combined organization score from all)
- $5x_0 + 5x_2 \geq 25$ (Total combined work quality rating from Dale and Laura)
- $5x_0 + 5x_1 \geq 25$ (Total combined work quality rating from Dale and Peggy)
- $5x_1 + 8x_2 \geq 21$ (Total combined work quality rating from Peggy and Laura)
- $5x_0 + 5x_1 + 8x_2 \geq 21$ (Total combined work quality rating from all)
- $4x_0 + 5x_2 \geq 8$ (Total combined computer competence rating from Dale and Laura)
- $4x_0 + 3x_1 \geq 22$ (Total combined computer competence rating from Dale and Peggy)
- $3x_1 + 5x_2 \geq 11$ (Total combined computer competence rating from Peggy and Laura)
- $4x_0 + 3x_1 + 5x_2 \geq 11$ (Total combined computer competence rating from all)
- $-x_0 + 5x_2 \geq 0$ (Constraint involving Dale and Laura)
- $3x_1 + 2x_2 \leq 46$ (Total combined likelihood to quit index from Peggy and Laura)
- $7x_0 + 2x_2 \leq 41$ (Total combined likelihood to quit index from Dale and Laura)
- $6x_1 + 7x_2 \leq 88$ (Total combined organization score from Peggy and Laura)
- $3x_0 + 6x_1 \leq 51$ (Total combined organization score from Dale and Peggy)
- $3x_0 + 7x_2 \leq 89$ (Total combined organization score from Dale and Laura)
- $4x_0 + 3x_1 \leq 36$ (Total combined computer competence rating from Dale and Peggy)
- $4x_0 + 3x_1 + 5x_2 \leq 28$ (Total combined computer competence rating from all)

## 3: Convert the problem into Gurobi code
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(lb=0, name="hours_worked_by_Dale")  # hours worked by Dale
    x1 = model.addVar(lb=0, name="hours_worked_by_Peggy")  # hours worked by Peggy
    x2 = model.addVar(lb=0, name="hours_worked_by_Laura")  # hours worked by Laura

    # Define the objective function
    model.setObjective(5 * x0 + 1 * x1 + 5 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(7 * x0 <= 7, name="Dale_likelihood_to_quit")
    model.addConstr(3 * x0 <= 3, name="Dale_organization_score")
    model.addConstr(5 * x0 <= 5, name="Dale_work_quality_rating")
    model.addConstr(4 * x0 <= 4, name="Dale_computer_competence_rating")

    model.addConstr(3 * x1 <= 3, name="Peggy_likelihood_to_quit")
    model.addConstr(6 * x1 <= 6, name="Peggy_organization_score")
    model.addConstr(5 * x1 <= 5, name="Peggy_work_quality_rating")
    model.addConstr(3 * x1 <= 3, name="Peggy_computer_competence_rating")

    model.addConstr(2 * x2 <= 2, name="Laura_likelihood_to_quit")
    model.addConstr(7 * x2 <= 7, name="Laura_organization_score")
    model.addConstr(8 * x2 <= 8, name="Laura_work_quality_rating")
    model.addConstr(5 * x2 <= 5, name="Laura_computer_competence_rating")

    model.addConstr(3 * x1 + 2 * x2 >= 8, name="combined_likelihood_to_quit_Peggy_Laura")
    model.addConstr(7 * x0 + 3 * x1 + 2 * x2 >= 8, name="combined_likelihood_to_quit_all")
    model.addConstr(3 * x0 + 7 * x2 >= 34, name="combined_organization_score_Dale_Laura")
    model.addConstr(3 * x0 + 6 * x1 + 7 * x2 >= 33, name="combined_organization_score_all")
    model.addConstr(5 * x0 + 5 * x2 >= 25, name="combined_work_quality_rating_Dale_Laura")
    model.addConstr(5 * x0 + 5 * x1 >= 25, name="combined_work_quality_rating_Dale_Peggy")
    model.addConstr(5 * x1 + 8 * x2 >= 21, name="combined_work_quality_rating_Peggy_Laura")
    model.addConstr(5 * x0 + 5 * x1 + 8 * x2 >= 21, name="combined_work_quality_rating_all")
    model.addConstr(4 * x0 + 5 * x2 >= 8, name="combined_computer_competence_rating_Dale_Laura")
    model.addConstr(4 * x0 + 3 * x1 >= 22, name="combined_computer_competence_rating_Dale_Peggy")
    model.addConstr(3 * x1 + 5 * x2 >= 11, name="combined_computer_competence_rating_Peggy_Laura")
    model.addConstr(4 * x0 + 3 * x1 + 5 * x2 >= 11, name="combined_computer_competence_rating_all")

    model.addConstr(-x0 + 5 * x2 >= 0, name="Dale_Laura_constraint")
    model.addConstr(3 * x1 + 2 * x2 <= 46, name="combined_likelihood_to_quit_Peggy_Laura_upper")
    model.addConstr(7 * x0 + 2 * x2 <= 41, name="combined_likelihood_to_quit_Dale_Laura_upper")
    model.addConstr(6 * x1 + 7 * x2 <= 88, name="combined_organization_score_Peggy_Laura_upper")
    model.addConstr(3 * x0 + 6 * x1 <= 51, name="combined_organization_score_Dale_Peggy_upper")
    model.addConstr(3 * x0 + 7 * x2 <= 89, name="combined_organization_score_Dale_Laura_upper")
    model.addConstr(4 * x0 + 3 * x1 <= 36, name="combined_computer_competence_rating_Dale_Peggy_upper")
    model.addConstr(4 * x0 + 3 * x1 + 5 * x2 <= 28, name="combined_computer_competence_rating_all_upper")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("Hours worked by Dale: ", x0.varValue)
        print("Hours worked by Peggy: ", x1.varValue)
        print("Hours worked by Laura: ", x2.varValue)
    else:
        print("The model is infeasible")

optimize_problem()
```