## Step 1: Define the symbolic representation of the problem
The problem involves minimizing an objective function subject to various constraints. The variables are:
- $x_0$: hours worked by Peggy
- $x_1$: hours worked by John
- $x_2$: hours worked by Dale
- $x_3$: hours worked by Laura
- $x_4$: hours worked by Bill
- $x_5$: hours worked by Ringo
- $x_6$: hours worked by George

## Step 2: Formulate the objective function
The objective function to minimize is:
$2.61x_0 + 5.96x_1 + 2.97x_2 + 9.77x_3 + 8.75x_4 + 2.62x_5 + 4.41x_6$

## Step 3: List the constraints
Constraints include:
- $2.69x_0 \geq 0$ (Peggy's productivity rating)
- $0.28x_0 \geq 0$ (Peggy's dollar cost per hour)
- $2.27x_0 \geq 0$ (Peggy's likelihood to quit index)
- $1.04x_0 \geq 0$ (Peggy's computer competence rating)
- $0.46x_0 \geq 0$ (Peggy's organization score)
- Similar constraints for other employees...
- $1.01x_2 + 1.18x_4 \geq 8$ (Dale and Bill's productivity)
- $1.18x_4 + 1.6x_5 \geq 12$ (Bill and Ringo's productivity)
- $1.18x_4 + 0.8x_6 \geq 19$ (Bill and George's productivity)
- ... (many more constraints)

## 4: Convert to Gurobi code
```python
import gurobi as gp

# Define variables
x = gp.multidict(['Peggy', 'John', 'Dale', 'Laura', 'Bill', 'Ringo', 'George'])[0]

# Objective function
model = gp.Model()
model.setObjective(2.61*x[0] + 5.96*x[1] + 2.97*x[2] + 9.77*x[3] + 8.75*x[4] + 2.62*x[5] + 4.41*x[6], gp.GRB.MINIMIZE)

# Constraints
# ... add all constraints here, for example:
model.addConstr(2.69*x[0] >= 0)
model.addConstr(0.28*x[0] >= 0)
# ... many more

# Add specific constraints as given
model.addConstr(1.01*x[2] + 1.18*x[4] >= 8)
model.addConstr(1.18*x[4] + 1.6*x[5] >= 12)
model.addConstr(1.18*x[4] + 0.8*x[6] >= 19)

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print('Objective: %g' % model.objval)
    for i in range(7):
        print('x[%d] = %g' % (i, x[i].x))
else:
    print('No solution found')
```