```json
{
  "sym_variables": [
    ("x0", "water purification units"),
    ("x1", "pathfinder teams"),
    ("x2", "light infantry companies"),
    ("x3", "logistics companies")
  ],
  "objective_function": "9.92 * x0 + 2.69 * x1 + 8.0 * x2 + 8.12 * x3",
  "constraints": [
    "12 * x0 + 9 * x1 + 11 * x2 + 4 * x3 <= 199",
    "9 * x0 + 4 * x1 + 6 * x2 + 13 * x3 <= 234",
    "11 * x2 + 4 * x3 >= 31",
    "12 * x0 + 11 * x2 >= 39",
    "9 * x1 + 4 * x3 >= 29",
    "12 * x0 + 4 * x3 >= 40",
    "12 * x0 + 9 * x1 + 4 * x3 >= 35",
    "6 * x2 + 13 * x3 >= 25",
    "9 * x0 + 13 * x3 >= 30",
    "11 * x2 + 4 * x3 <= 152",
    "9 * x1 + 11 * x2 <= 81",
    "12 * x0 + 11 * x2 <= 143",
    "9 * x1 + 4 * x3 <= 155",
    "12 * x0 + 4 * x3 <= 110",
    "12 * x0 + 9 * x1 + 11 * x2 + 4 * x3 <= 110",
    "4 * x1 + 6 * x2 <= 210",
    "6 * x2 + 13 * x3 <= 197",
    "9 * x0 + 4 * x1 + 6 * x2 <= 73",
    "9 * x0 + 4 * x1 + 6 * x2 + 13 * x3 <= 73",
    "x0, x1, x2, x3 are integers"

  ]
}
```

```python
import gurobipy as gp

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

    # Create variables
    x = m.addVars(4, lb=0, vtype=gp.GRB.INTEGER, name=["water_purification", "pathfinder_teams", "light_infantry", "logistics_companies"])


    # Set objective function
    m.setObjective(9.92 * x[0] + 2.69 * x[1] + 8.0 * x[2] + 8.12 * x[3], gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(12 * x[0] + 9 * x[1] + 11 * x[2] + 4 * x[3] <= 199, "logistics_footprint_limit")
    m.addConstr(9 * x[0] + 4 * x[1] + 6 * x[2] + 13 * x[3] <= 234, "mobility_rating_limit")
    m.addConstr(11 * x[2] + 4 * x[3] >= 31, "c1")
    m.addConstr(12 * x[0] + 11 * x[2] >= 39, "c2")
    m.addConstr(9 * x[1] + 4 * x[3] >= 29, "c3")
    m.addConstr(12 * x[0] + 4 * x[3] >= 40, "c4")
    m.addConstr(12 * x[0] + 9 * x[1] + 4 * x[3] >= 35, "c5")
    m.addConstr(6 * x[2] + 13 * x[3] >= 25, "c6")
    m.addConstr(9 * x[0] + 13 * x[3] >= 30, "c7")
    m.addConstr(11 * x[2] + 4 * x[3] <= 152, "c8")
    m.addConstr(9 * x[1] + 11 * x[2] <= 81, "c9")
    m.addConstr(12 * x[0] + 11 * x[2] <= 143, "c10")
    m.addConstr(9 * x[1] + 4 * x[3] <= 155, "c11")
    m.addConstr(12 * x[0] + 4 * x[3] <= 110, "c12")
    m.addConstr(12 * x[0] + 9 * x[1] + 11 * x[2] + 4 * x[3] <= 110, "c13")
    m.addConstr(4 * x[1] + 6 * x[2] <= 210, "c14")
    m.addConstr(6 * x[2] + 13 * x[3] <= 197, "c15")
    m.addConstr(9 * x[0] + 4 * x[1] + 6 * x[2] <= 73, "c16")
    m.addConstr(9 * x[0] + 4 * x[1] + 6 * x[2] + 13 * x[3] <= 73, "c17")


    # Optimize model
    m.optimize()

    if m.status == gp.GRB.OPTIMAL:
        for v in m.getVars():
            print(f'{v.varName}: {v.x}')
        print(f'Obj: {m.objVal}')
    elif m.status == gp.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print(f"Optimization ended with status {m.status}")


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

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