To tackle this problem, we'll break it down into its components as requested: a symbolic representation of the problem and the corresponding Gurobi code to solve it.

### Symbolic Representation

Given:
- Variables: `x1` represents 'hours worked by Mary', `x2` represents 'hours worked by George'.
- Objective Function: Maximize `2.09*x1 + 2.23*x2`.
- Constraints:
  1. `3*x1 + 10*x2 >= 33` (Total combined paperwork competence rating is at least 33).
  2. `5*x1 - 6*x2 >= 0` (Five times the hours worked by Mary minus six times the hours worked by George is no less than zero).
  3. `3*x1 + 10*x2 <= 169` (Total combined paperwork competence rating is at most 169).

The symbolic representation in JSON format as requested is:

```json
{
  'sym_variables': [('x1', 'hours worked by Mary'), ('x2', 'hours worked by George')],
  'objective_function': 'Maximize 2.09*x1 + 2.23*x2',
  'constraints': [
    '3*x1 + 10*x2 >= 33',
    '5*x1 - 6*x2 >= 0',
    '3*x1 + 10*x2 <= 169'
  ]
}
```

### Gurobi Code

Now, let's implement this problem in Python using the Gurobi solver. We need to ensure that `x1` can be a non-integer and `x2` must be an integer.

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name='hours_worked_by_Mary')
x2 = m.addVar(vtype=GRB.INTEGER, name='hours_worked_by_George')

# Set objective function
m.setObjective(2.09*x1 + 2.23*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(3*x1 + 10*x2 >= 33, 'paperwork_rating_min')
m.addConstr(5*x1 - 6*x2 >= 0, 'mary_george_ratio')
m.addConstr(3*x1 + 10*x2 <= 169, 'paperwork_rating_max')

# Optimize the model
m.optimize()

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