```json
{
  "sym_variables": [
    ("x0", "water purification units"),
    ("x1", "transportation companies")
  ],
  "objective_function": "9*x0**2 + 3*x0 + 8*x1",
  "constraints": [
    "17*x0 + 4*x1 <= 120",  // Logistics footprint upper bound
    "16*x0 + 18*x1 <= 100",  // Mobility rating upper bound
    "x0**2 + x1**2 >= 25",      // Total logistics footprint squared lower bound
    "16*x0 + 18*x1 >= 25",      // Total mobility rating lower bound
    "-x0 + 8*x1 >= 0",         // Constraint on x0 and x1
    "x0**2 + x1**2 <= 93",      // Total logistics footprint squared upper bound
    "17*x0 + 4*x1 <= 93",      // Total logistics footprint upper bound (redundant with first constraint)
    "16*x0 + 18*x1 <= 91",      // Total mobility rating upper bound (redundant with second constraint)
    "x0 == int(x0)",  // Integer constraint for x0
    "x1 == int(x1)"   // Integer constraint for x1
  ]
}
```

The provided constraints include some redundancies.  For example, the maximum logistics footprint constraint is given twice (<= 120 and <= 93). The tighter constraint (<= 93) will be the effective one. Similarly, the mobility rating upper bound is presented as <= 100 and <= 91, with the latter being the active constraint.

```python
import gurobipy as gp

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

    # Create variables
    x0 = model.addVar(vtype=gp.GRB.INTEGER, name="water_purification_units")
    x1 = model.addVar(vtype=gp.GRB.INTEGER, name="transportation_companies")

    # Set objective function
    model.setObjective(9*x0**2 + 3*x0 + 8*x1, gp.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(17*x0 + 4*x1 <= 93, "logistics_footprint_upper_bound")  # Using the tighter constraint
    model.addConstr(16*x0 + 18*x1 <= 91, "mobility_rating_upper_bound")  # Using the tighter constraint
    model.addConstr(x0**2 + x1**2 >= 25, "logistics_footprint_squared_lower_bound")
    model.addConstr(16*x0 + 18*x1 >= 25, "mobility_rating_lower_bound")
    model.addConstr(-x0 + 8*x1 >= 0, "x0_x1_constraint")
    model.addConstr(x0**2 + x1**2 <= 93, "logistics_footprint_squared_upper_bound")


    # Optimize model
    model.optimize()

    # Print results
    if model.status == gp.GRB.OPTIMAL:
        print('Obj: %g' % model.objVal)
        print('water_purification_units:', x0.x)
        print('transportation_companies:', x1.x)
    elif model.status == gp.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print(f"Optimization ended with status {model.status}")


except gp.GurobiError as e:
    print('Error code ' + str(e.errno) + ': ' + str(e))

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