To solve this optimization problem and convert it into Gurobi code, let's first break down the given information:

- Variables: `diapers` and `candles`
- Resources/Attributes:
  - Weight (`r0`): Diapers weigh 4 lbs each, candles weigh 7 lbs each. The total weight should be at least 22 lbs but no more than 44 lbs.
  - Sustainability score (`r1`): Diapers have a sustainability score of 1, candles have a score of 3. The combined score must be at least 17 and at most 26.
  - Usefulness rating (`r2`): Diapers have a usefulness rating of 6, candles have a rating of 7. The total rating should be at least 40 but no more than 71.
  - Portability rating (`r3`): Diapers have a portability rating of 4, candles have a rating of 1. The combined rating must be at least 25 and at most 29.

Objective Function: Minimize `1.81 * diapers + 3.86 * candles`

Constraints:
- Combined weight is at least 22 lbs.
- Combined sustainability score is at least 17.
- Combined usefulness rating is at least 40.
- Combined portability rating is at least 25.
- Eight times the number of diapers plus -3 times the number of candles should be at minimum zero.
- Total weight is no more than 44 lbs.
- Total combined sustainability score is at most 26.
- Total combined usefulness rating is at most 71.
- Total combined portability rating is at most 29.
- Both `diapers` and `candles` must be whole numbers.

Let's create a symbolic representation of the problem:

```json
{
  'sym_variables': [('x1', 'diapers'), ('x2', 'candles')],
  'objective_function': 'Minimize 1.81*x1 + 3.86*x2',
  'constraints': [
    '4*x1 + 7*x2 >= 22', 
    '4*x1 + 7*x2 <= 44', 
    'x1 + 3*x2 >= 17', 
    'x1 + 3*x2 <= 26', 
    '6*x1 + 7*x2 >= 40', 
    '6*x1 + 7*x2 <= 71', 
    '4*x1 + x2 >= 25', 
    '4*x1 + x2 <= 29', 
    '8*x1 - 3*x2 >= 0'
  ]
}
```

Now, let's implement this in Gurobi Python:

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(vtype=GRB.INTEGER, name="diapers")
x2 = m.addVar(vtype=GRB.INTEGER, name="candles")

# Set the objective function
m.setObjective(1.81*x1 + 3.86*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(4*x1 + 7*x2 >= 22, "weight_min")
m.addConstr(4*x1 + 7*x2 <= 44, "weight_max")
m.addConstr(x1 + 3*x2 >= 17, "sustainability_min")
m.addConstr(x1 + 3*x2 <= 26, "sustainability_max")
m.addConstr(6*x1 + 7*x2 >= 40, "usefulness_min")
m.addConstr(6*x1 + 7*x2 <= 71, "usefulness_max")
m.addConstr(4*x1 + x2 >= 25, "portability_min")
m.addConstr(4*x1 + x2 <= 29, "portability_max")
m.addConstr(8*x1 - 3*x2 >= 0, "diapers_candles_ratio")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Diapers: {x1.x}")
    print(f"Candles: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```