## Step 1: Define the symbolic representation of the variables
The variables are 'hours worked by Dale' and 'hours worked by George', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Define the symbolic representation of the problem
The problem has the following resources/attributes:
- $r_0$: paperwork competence rating
- $r_1$: dollar cost per hour
- $r_2$: work quality rating
- $r_3$: likelihood to quit index

With the given values:
- $x_{0,1} = 7.09$, $x_{0,2} = 5.38$ for $r_0$
- $x_{1,1} = 4.11$, $x_{1,2} = 6.11$ for $r_1$
- $x_{2,1} = 2.68$, $x_{2,2} = 4.9$ for $r_2$
- $x_{3,1} = 0.85$, $x_{3,2} = 3.05$ for $r_3$

## 3: Formulate the objective function
The objective function to minimize is $7x_1 + 4x_2$.

## 4: Formulate the constraints
1. $7.09x_1 + 5.38x_2 \geq 49$
2. $4.11x_1 + 6.11x_2 \geq 45$
3. $2.68x_1 + 4.9x_2 \geq 95$
4. $0.85x_1 + 3.05x_2 \geq 63$
5. $-10x_1 + 3x_2 \geq 0$
6. $7.09x_1 + 5.38x_2 \leq 119$
7. $4.11x_1 + 6.11x_2 \leq 109$
8. $2.68x_1 + 4.9x_2 \leq 169$
9. $0.85x_1 + 3.05x_2 \leq 109$
10. $x_1 \geq 0$ (Implicit, as hours cannot be negative)
11. $x_2 \mod 1 = 0$ or simply $x_2$ is an integer (since you cannot have a fractional amount of hours worked by George)

## 5: Convert the problem into Gurobi code
We will use Gurobi's Python API to model and solve this problem.

```python
import gurobi as gp

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

# Define the variables
x1 = m.addVar(name="hours_worked_by_Dale", lb=0)  # Dale can work fractional hours
x2 = m.addVar(name="hours_worked_by_George", vtype=gp.GRB.INTEGER)  # George can only work integer hours

# Define the objective function
m.setObjective(7 * x1 + 4 * x2, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(7.09 * x1 + 5.38 * x2 >= 49)
m.addConstr(4.11 * x1 + 6.11 * x2 >= 45)
m.addConstr(2.68 * x1 + 4.9 * x2 >= 95)
m.addConstr(0.85 * x1 + 3.05 * x2 >= 63)
m.addConstr(-10 * x1 + 3 * x2 >= 0)
m.addConstr(7.09 * x1 + 5.38 * x2 <= 119)
m.addConstr(4.11 * x1 + 6.11 * x2 <= 109)
m.addConstr(2.68 * x1 + 4.9 * x2 <= 169)
m.addConstr(0.85 * x1 + 3.05 * x2 <= 109)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Hours worked by Dale: {x1.varValue}")
    print(f"Hours worked by George: {x2.varValue}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```

## 6: Symbolic Representation
The symbolic representation of the problem is as follows:

```json
{
    'sym_variables': [
        ('x1', 'hours worked by Dale'),
        ('x2', 'hours worked by George')
    ],
    'objective_function': '7*x1 + 4*x2',
    'constraints': [
        '7.09*x1 + 5.38*x2 >= 49',
        '4.11*x1 + 6.11*x2 >= 45',
        '2.68*x1 + 4.9*x2 >= 95',
        '0.85*x1 + 3.05*x2 >= 63',
        '-10*x1 + 3*x2 >= 0',
        '7.09*x1 + 5.38*x2 <= 119',
        '4.11*x1 + 6.11*x2 <= 109',
        '2.68*x1 + 4.9*x2 <= 169',
        '0.85*x1 + 3.05*x2 <= 109'
    ]
}
```