The provided constraints contain some redundancies (e.g., the fiber and protein minimum constraints are stated twice). We've consolidated these in the code below.  The upper bounds on resources (fiber, protein, fat) are handled using the resource dictionaries. The objective function and remaining constraints are translated directly into Gurobi code.

```python
import gurobipy as gp
from gurobipy import GRB

try:
    # Create a new model
    m = gp.Model("resource_allocation")

    # Create variables
    fruit_salads = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="fruit_salads")
    cantaloupes = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="cantaloupes")

    # Set objective function
    m.setObjective(6 * fruit_salads + 2 * cantaloupes, GRB.MINIMIZE)

    # Add constraints
    m.addConstr(3 * fruit_salads + 15 * cantaloupes >= 57, "fiber_min")
    m.addConstr(11 * fruit_salads + 17 * cantaloupes >= 61, "protein_min")
    m.addConstr(1 * fruit_salads + 21 * cantaloupes >= 77, "fat_min")
    m.addConstr(-10 * fruit_salads + 2 * cantaloupes >= 0, "custom_constraint")

    m.addConstr(3 * fruit_salads + 15 * cantaloupes <= 285, "fiber_max")
    m.addConstr(11 * fruit_salads + 17 * cantaloupes <= 252, "protein_max") # Using the correct upper bound from the provided data
    m.addConstr(1 * fruit_salads + 21 * cantaloupes <= 224, "fat_max") # Using the correct upper bound from the provided data


    # Optimize model
    m.optimize()

    # Print results
    if m.status == GRB.OPTIMAL:
        print(f"Optimal objective value: {m.objVal}")
        print(f"Number of fruit salads: {fruit_salads.x}")
        print(f"Number of cantaloupes: {cantaloupes.x}")
    elif m.status == GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print(f"Optimization ended with status {m.status}")


except gp.GurobiError as e:
    print(f"Error code {e.errno}: {e}")

except AttributeError:
    print('Encountered an attribute error')
```
