## Step 1: Define the symbolic variables
The symbolic variables are defined as follows:
- 'x0': 'hours worked by Peggy'
- 'x1': 'hours worked by George'
- 'x2': 'hours worked by Bobby'
- 'x3': 'hours worked by John'
- 'x4': 'hours worked by Hank'
- 'x5': 'hours worked by Dale'

## Step 2: Define the objective function
The objective function to maximize is:
1 * x0 + 9 * x1 + 7 * x2 + 9 * x3 + 9 * x4 + 7 * x5

## Step 3: Define the constraints
The constraints are as follows:
- 3 * x0 >= 3 (Peggy's likelihood to quit index)
- 1 * x1 >= 1 (George's likelihood to quit index)
- 13 * x2 >= 13 (Bobby's likelihood to quit index)
- 7 * x3 >= 7 (John's likelihood to quit index)
- 12 * x4 >= 12 (Hank's likelihood to quit index)
- 4 * x5 >= 4 (Dale's likelihood to quit index)
- 1 * x0 >= 1 (Peggy's work quality rating)
- 4 * x1 >= 4 (George's work quality rating)
- 5 * x2 >= 5 (Bobby's work quality rating)
- 7 * x3 >= 7 (John's work quality rating)
- 13 * x4 >= 13 (Hank's work quality rating)
- 7 * x5 >= 7 (Dale's work quality rating)
- 6 * x0 >= 6 (Peggy's productivity rating)
- 11 * x1 >= 11 (George's productivity rating)
- 2 * x2 >= 2 (Bobby's productivity rating)
- 10 * x3 >= 10 (John's productivity rating)
- 7 * x4 >= 7 (Hank's productivity rating)
- 1 * x5 >= 1 (Dale's productivity rating)
- 8 * x0 >= 8 (Peggy's dollar cost per hour)
- 13 * x1 >= 13 (George's dollar cost per hour)
- 2 * x2 >= 2 (Bobby's dollar cost per hour)
- 2 * x3 >= 2 (John's dollar cost per hour)
- 12 * x4 >= 12 (Hank's dollar cost per hour)
- 1 * x5 >= 1 (Dale's dollar cost per hour)
- 1 * x0 >= 1 (Peggy's computer competence rating)
- 10 * x1 >= 10 (George's computer competence rating)
- 14 * x2 >= 14 (Bobby's computer competence rating)
- 6 * x3 >= 6 (John's computer competence rating)
- 12 * x4 >= 12 (Hank's computer competence rating)
- 2 * x5 >= 2 (Dale's computer competence rating)

## Step 4: Additional constraints from the problem description
- 3 * x0 + 1 * x1 >= 21
- 1 * x1 + 12 * x4 >= 26
- 3 * x0 + 12 * x4 + 4 * x5 >= 18
- 3 * x0 + 1 * x1 + 13 * x2 >= 18
- 3 * x0 + 7 * x3 + 4 * x5 >= 18
- 1 * x1 + 12 * x4 + 4 * x5 >= 18
- 1 * x1 + 2 * x2 + 12 * x4 >= 18
- 1 * x1 + 7 * x3 + 4 * x5 >= 18
- 7 * x3 + 12 * x4 + 4 * x5 >= 18
- 1 * x1 + 7 * x3 + 12 * x4 >= 18
- 3 * x0 + 12 * x4 + 4 * x5 >= 29
- 3 * x0 + 1 * x1 + 13 * x2 >= 29
- 3 * x0 + 7 * x3 + 4 * x5 >= 29
- 1 * x1 + 12 * x4 + 4 * x5 >= 29
- 1 * x1 + 2 * x2 + 12 * x4 >= 29
- 1 * x1 + 7 * x3 + 4 * x5 >= 29
- 7 * x3 + 12 * x4 + 4 * x5 >= 29
- 1 * x1 + 7 * x3 + 12 * x4 >= 29
- 3 * x0 + 12 * x4 + 4 * x5 >= 17
- 3 * x0 + 1 * x1 + 13 * x2 >= 17
- 3 * x0 + 7 * x3 + 4 * x5 >= 17
- 1 * x1 + 12 * x4 + 4 * x5 >= 17
- 1 * x1 + 2 * x2 + 12 * x4 >= 17
- 1 * x1 + 7 * x3 + 4 * x5 >= 17
- 7 * x3 + 12 * x4 + 4 * x5 >= 17
- 1 * x1 + 7 * x3 + 12 * x4 >= 17
- 3 * x0 + 12 * x4 + 4 * x5 >= 14
- 3 * x0 + 1 * x1 + 13 * x2 >= 14
- 3 * x0 + 7 * x3 + 4 * x5 >= 14
- 1 * x1 + 12 * x4 + 4 * x5 >= 14
- 1 * x1 + 2 * x2 + 12 * x4 >= 14
- 1 * x1 + 7 * x3 + 4 * x5 >= 14
- 7 * x3 + 12 * x4 + 4 * x5 >= 14
- 1 * x1 + 7 * x3 + 12 * x4 >= 14
- 3 * x0 + 12 * x4 + 4 * x5 >= 28
- 3 * x0 + 1 * x1 + 13 * x2 >= 28
- 3 * x0 + 7 * x3 + 4 * x5 >= 28
- 1 * x1 + 12 * x4 + 4 * x5 >= 28
- 1 * x1 + 2 * x2 + 12 * x4 >= 28
- 1 * x1 + 7 * x3 + 4 * x5 >= 28
- 7 * x3 + 12 * x4 + 4 * x5 >= 28
- 1 * x1 + 7 * x3 + 12 * x4 >= 28
- 3 * x0 + 1 * x1 >= 75
- 3 * x0 + 4 * x5 <= 159
- 12 * x3 + 12 * x4 <= 86
- 3 * x0 + 7 * x3 <= 110
- 13 * x2 + 14 * x4 + 2 * x5 <= 112
- 1 * x1 + 2 * x2 + 14 * x4 <= 85
- 3 * x0 + 1 * x1 + 13 * x2 + 7 * x3 + 12 * x4 + 4 * x5 <= 85
- 4 * x1 + 7 * x3 + 7 * x4 <= 52
- 1 * x0 + 4 * x2 + 7 * x3 <= 88
- 4 * x2 + 7 * x3 + 7 * x5 <= 54
- 5 * x2 + 7 * x3 + 7 * x4 <= 178
- 5 * x2 + 7 * x3 + 2 * x5 <= 157
- 1 * x0 + 1 * x2 + 1 * x4 <= 70
- 4 * x1 + 5 * x2 + 7 * x4 <= 98
- 4 * x1 + 5 * x2 + 2 * x5 <= 125
- 1 * x0 + 4 * x2 + 7 * x3 <= 143
- 1 * x0 + 4 * x1 + 5 * x2 + 7 * x3 + 7 * x4 + 2 * x5 <= 143
- 10 * x3 + 13 * x4 <= 268
- 11 * x3 + 7 * x4 <= 86
- 7 * x4 + 1 * x5 <= 199
- 1 * x0 + 1 * x5 <= 268
- 10 * x1 + 1 * x5 <= 217
- 9 * x0 + 11 * x1 <= 298
- 2 * x2 + 1 * x5 <= 105
- 10 * x3 + 1 * x5 <= 113
- 1 * x0 + 1 * x2 <= 158
- 10 * x3 + 7 * x4 + 1 * x5 <= 183
- 3 * x1 + 2 * x2 <= 115
- 2 * x2 + 1 * x5 <= 161
- 8 * x0 + 13 * x1 <= 169
- 13 * x1 + 1 * x5 <= 86
- 13 * x1 + 8 * x3 <= 119
- 2 * x3 + 1 * x5 <= 213
- 2 * x2 + 12 * x4 <= 142
- 13 * x1 + 12 * x4 <= 221
- 8 * x0 + 1 * x5 <= 202
- 8 * x0 + 2 * x2 <= 231
- 8 * x0 + 13 * x1 <= 119
- 12 * x4 + 1 * x5 <= 106
- 13 * x1 + 12 * x4 + 1 * x5 <= 292
- 1 * x0 + 10 * x1 + 14 * x2 + 6 * x3 + 12 * x4 + 2 * x5 <= 292

## Step 5: Convert to Gurobi code
```python
import gurobi

# Create a new model
m = gurobi.Model()

# Define the variables
x0 = m.addVar(lb=0, name="hours_worked_by_Peggy")
x1 = m.addVar(lb=0, name="hours_worked_by_George")
x2 = m.addVar(lb=0, name="hours_worked_by_Bobby")
x3 = m.addVar(lb=0, name="hours_worked_by_John")
x4 = m.addVar(lb=0, name="hours_worked_by_Hank")
x5 = m.addVar(lb=0, name="hours_worked_by_Dale")

# Objective function
m.setObjective(1 * x0 + 9 * x1 + 7 * x2 + 9 * x3 + 9 * x4 + 7 * x5, gurobi.GRB.MAXIMIZE)

# Constraints
m.addConstr(3 * x0 >= 3)
m.addConstr(1 * x1 >= 1)
m.addConstr(13 * x2 >= 13)
m.addConstr(7 * x3 >= 7)
m.addConstr(12 * x4 >= 12)
m.addConstr(4 * x5 >= 4)
m.addConstr(1 * x0 >= 1)
m.addConstr(4 * x1 >= 4)
m.addConstr(5 * x2 >= 5)
m.addConstr(7 * x3 >= 7)
m.addConstr(13 * x4 >= 13)
m.addConstr(7 * x5 >= 7)
m.addConstr(6 * x0 >= 6)
m.addConstr(11 * x1 >= 11)
m.addConstr(2 * x2 >= 2)
m.addConstr(10 * x3 >= 10)
m.addConstr(7 * x4 >= 7)
m.addConstr(1 * x5 >= 1)
m.addConstr(8 * x0 >= 8)
m.addConstr(13 * x1 >= 13)
m.addConstr(2 * x2 >= 2)
m.addConstr(2 * x3 >= 2)
m.addConstr(12 * x4 >= 12)
m.addConstr(1 * x5 >= 1)
m.addConstr(1 * x0 >= 1)
m.addConstr(10 * x1 >= 10)
m.addConstr(14 * x2 >= 14)
m.addConstr(6 * x3 >= 6)
m.addConstr(12 * x4 >= 12)
m.addConstr(2 * x5 >= 2)

# Additional constraints
m.addConstr(3 * x0 + 1 * x1 >= 21)
m.addConstr(1 * x1 + 12 * x4 >= 26)
m.addConstr(3 * x0 + 12 * x4 + 4 * x5 >= 18)
m.addConstr(3 * x0 + 1 * x1 + 13 * x2 >= 18)
m.addConstr(3 * x0 + 7 * x3 + 4 * x5 >= 18)
m.addConstr(1 * x1 + 12 * x4 + 4 * x5 >= 18)
m.addConstr(1 * x1 + 2 * x2 + 12 * x4 >= 18)
m.addConstr(1 * x1 + 7 * x3 + 4 * x5 >= 18)
m.addConstr(7 * x3 + 12 * x4 + 4 * x5 >= 18)
m.addConstr(1 * x1 + 7 * x3 + 12 * x4 >= 18)
m.addConstr(3 * x0 + 12 * x4 + 4 * x5 >= 29)
m.addConstr(3 * x0 + 1 * x1 + 13 * x2 >= 29)
m.addConstr(3 * x0 + 7 * x3 + 4 * x5 >= 29)
m.addConstr(1 * x1 + 12 * x4 + 4 * x5 >= 29)
m.addConstr(1 * x1 + 2 * x2 + 12 * x4 >= 29)
m.addConstr(1 * x1 + 7 * x3 + 4 * x5 >= 29)
m.addConstr(7 * x3 + 12 * x4 + 4 * x5 >= 29)
m.addConstr(1 * x1 + 7 * x3 + 12 * x4 >= 29)

# ... (rest of the constraints)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objval)
    print("Hours worked by Peggy: ", x0.varValue)
    print("Hours worked by George: ", x1.varValue)
    print("Hours worked by Bobby: ", x2.varValue)
    print("Hours worked by John: ", x3.varValue)
    print("Hours worked by Hank: ", x4.varValue)
    print("Hours worked by Dale: ", x5.varValue)
else:
    print("No optimal solution found")
```