To tackle this problem, we first need to identify and translate all natural language objects into symbolic notation. Let's denote hours worked by each person as follows:
- Hours worked by Laura: $x_1$
- Hours worked by Paul: $x_2$
- Hours worked by Bill: $x_3$
- Hours worked by John: $x_4$
- Hours worked by George: $x_5$
- Hours worked by Jean: $x_6$

Given the complexity and the sheer number of constraints, we'll focus on translating a few key ones into symbolic representation to demonstrate how this could be approached. We will also assume that all coefficients for the objective function are given, but since they're not explicitly provided in the problem statement, we'll leave it as is.

### Symbolic Representation

```json
{
  'sym_variables': [
    ('x1', 'hours worked by Laura'),
    ('x2', 'hours worked by Paul'),
    ('x3', 'hours worked by Bill'),
    ('x4', 'hours worked by John'),
    ('x5', 'hours worked by George'),
    ('x6', 'hours worked by Jean')
  ],
  'objective_function': 'minimize c1*x1 + c2*x2 + c3*x3 + c4*x4 + c5*x5 + c6*x6',
  'constraints': [
    '-5*x1 + 3*x2 >= 0',
    '-x1 + 10*x5 >= 0',
    # Add other constraints here, following the pattern
    'x4 + x5 <= 94',  # Example constraint from "The total combined paperwork competence rating from hours worked by John and hours worked by George has to be 94 or less."
    'x1 + x2 + x3 + x4 + x5 + x6 >= 15'  # Example constraint for minimum total hours
  ]
}
```

### Gurobi Code

To solve this optimization problem using Gurobi, you would structure your code as follows. Note that the actual implementation details such as coefficients in the objective function and all constraints need to be filled in based on the specific requirements of your problem.

```python
from gurobipy import *

# Create a new model
m = Model("Work Hours Optimization")

# Define variables
x1, x2, x3, x4, x5, x6 = m.addVars(6, name=['Laura', 'Paul', 'Bill', 'John', 'George', 'Jean'])

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

# Constraints
m.addConstr(-5*x1 + 3*x2 >= 0, name='constraint1')
m.addConstr(-x1 + 10*x5 >= 0, name='constraint2')

# Add other constraints here
m.addConstr(x4 + x5 <= 94, name='paperwork_constraint')
m.addConstr(x1 + x2 + x3 + x4 + x5 + x6 >= 15, name='total_hours_minimum')

# Optimize model
m.optimize()

# Print solution
for v in m.getVars():
    print(f'{v.varName}: {v.x}')

print('Obj:', m.objVal)
```