To create a symbolic representation of the problem and then implement it using Gurobi, we first need to define our variables and the objective function. Given the complexity of the problem description, let's simplify by focusing on key elements:

- **Variables**: Let `x1`, `x2`, ..., `x7` represent the hours worked by Dale, Peggy, Hank, John, Mary, and Laura, respectively.

- **Objective Function**: The objective is not explicitly stated in terms of minimization or maximization. For demonstration purposes, let's assume we want to minimize the total cost while satisfying all constraints. We'll need coefficients for each worker's hourly wage, but these are not provided. Let `c1`, `c2`, ..., `c6` be the costs per hour for Dale, Peggy, Hank, John, Mary, and Laura.

- **Constraints**: These include work quality, dollar cost per hour, computer competence ratings, organization scores, likelihood to quit indices, and more. Each constraint will involve combinations of our variables (`x1` through `x6`) with various coefficients and constants.

Given the lack of explicit objective function details in the problem statement, I'll provide a generic setup focusing on constraints related to work quality, cost, computer competence, organization score, and likelihood to quit index.

### Symbolic Representation

```json
{
    'sym_variables': [
        ('x1', 'hours worked by Dale'), 
        ('x2', 'hours worked by Peggy'), 
        ('x3', 'hours worked by Hank'), 
        ('x4', 'hours worked by John'), 
        ('x5', 'hours worked by Mary'), 
        ('x6', 'hours worked by Laura')
    ],
    'objective_function': 'Minimize c1*x1 + c2*x2 + c3*x3 + c4*x4 + c5*x5 + c6*x6',
    'constraints': [
        'x1 + x2 >= 42',  # Simplified example constraint
        '27 <= x4 + x5',  # Another simplified constraint
        '-x2 + 4*x3 >= 0',  # Example of the "3 times Peggy, minus 4 times Hank" constraint
        # Add other constraints as needed...
    ]
}
```

### Gurobi Code

```python
from gurobipy import *

# Create a new model
m = Model("Workforce_Optimization")

# Define variables
x1 = m.addVar(lb=0, name="Dale")
x2 = m.addVar(lb=0, name="Peggy")
x3 = m.addVar(lb=0, name="Hank")
x4 = m.addVar(lb=0, name="John")
x5 = m.addVar(lb=0, name="Mary")
x6 = m.addVar(lb=0, name="Laura")

# Objective function (example)
m.setObjective(x1 + 2*x2 + x3 + 3*x4 + x5 + x6, GRB.MINIMIZE)

# Constraints
m.addConstr(x1 + x2 >= 42)  # Example constraint
m.addConstr(27 <= x4 + x5)  # Another example constraint
m.addConstr(-x2 + 4*x3 >= 0)  # "3 times Peggy, minus 4 times Hank" constraint

# Add other constraints here...

# Optimize model
m.optimize()

# Print solution
for v in m.getVars():
    print('%s %g' % (v.varName, v.x))

print('Obj: %g' % m.objVal)
```