To tackle this problem, we first need to translate the given natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints in algebraic terms.

Given:
- Variables: 'hours worked by Dale' and 'hours worked by George'
- Resources/Attributes:
  - 'paperwork competence rating': with upper bounds and ratings for Dale and George
  - 'computer competence rating': with upper bounds and ratings for Dale and George

Let's define our symbolic variables as follows:
- \(x_1\): hours worked by Dale
- \(x_2\): hours worked by George

The objective function to maximize is: \(6x_1 + 2x_2\)

Now, let's translate the constraints into algebraic terms:

1. Total combined paperwork competence rating from hours worked by Dale and George should be at least 6:
   - \(2x_1 + 6x_2 \geq 6\)
2. Total combined computer competence rating from hours worked by Dale and George should be no less than 16:
   - \(4x_1 + 9x_2 \geq 16\)
3. \(-10x_1 + 6x_2 \geq 0\)
4. The total combined paperwork competence rating from hours worked by Dale plus hours worked by George should be 19 or less:
   - \(2x_1 + 6x_2 \leq 19\)
5. The constraint about the maximum paperwork competence rating is already covered in (4).
6. The total combined computer competence rating from hours worked by Dale and hours worked by George must be less than or equal to 28:
   - \(4x_1 + 9x_2 \leq 28\)

Given that there might be a non-integer number of hours worked by both Dale and George, we treat \(x_1\) and \(x_2\) as continuous variables.

Thus, our symbolic representation is:
```json
{
  'sym_variables': [('x1', 'hours worked by Dale'), ('x2', 'hours worked by George')],
  'objective_function': '6*x1 + 2*x2',
  'constraints': [
    '2*x1 + 6*x2 >= 6',
    '4*x1 + 9*x2 >= 16',
    '-10*x1 + 6*x2 >= 0',
    '2*x1 + 6*x2 <= 19',
    '4*x1 + 9*x2 <= 28'
  ]
}
```

Now, let's write the Gurobi code to solve this optimization problem:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="hours_worked_by_Dale")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="hours_worked_by_George")

# Set the objective function
m.setObjective(6*x1 + 2*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(2*x1 + 6*x2 >= 6, "paperwork_rating_min")
m.addConstr(4*x1 + 9*x2 >= 16, "computer_rating_min")
m.addConstr(-10*x1 + 6*x2 >= 0, "mixed_constraint")
m.addConstr(2*x1 + 6*x2 <= 19, "paperwork_rating_max")
m.addConstr(4*x1 + 9*x2 <= 28, "computer_rating_max")

# Optimize the model
m.optimize()

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