```json
{
  "sym_variables": [
    ("x0", "reconnaissance troops"),
    ("x1", "airborne infantry companies"),
    ("x2", "signal platoons"),
    ("x3", "transportation companies")
  ],
  "objective_function": "5*x0 + 2*x1 + 4*x2 + 8*x3",
  "constraints": [
    "4*x0 + 2*x1 + 5*x2 + 2*x3 <= 81",  // deployment weight
    "4*x0 + 5*x1 + 4*x2 + 1*x3 <= 41",  // logistical capacity
    "4*x0 + 2*x1 >= 11",
    "4*x0 + 2*x3 >= 7",
    "2*x1 + 2*x3 >= 18",
    "2*x1 + 5*x2 >= 7",
    "4*x0 + 1*x3 >= 10",
    "5*x1 + 4*x2 >= 7",
    "4*x0 + 5*x1 + 4*x2 >= 6",
    "4*x0 + 4*x2 + 1*x3 >= 6",
    "5*x1 + 4*x2 + 1*x3 >= 6",
    "4*x0 + 5*x1 + 1*x3 >= 6",
    "4*x0 + 5*x1 + 4*x2 >= 7",  
    "4*x0 + 4*x2 + 1*x3 >= 7",  
    "5*x1 + 4*x2 + 1*x3 >= 7",  
    "4*x0 + 5*x1 + 1*x3 >= 7",  
    "4*x0 + 5*x1 + 4*x2 >= 5",
    "4*x0 + 4*x2 + 1*x3 >= 5",
    "5*x1 + 4*x2 + 1*x3 >= 5",
    "4*x0 + 5*x1 + 1*x3 >= 5",
    "2*x1 + 2*x3 <= 27",
    "4*x0 + 5*x2 <= 74",
    "4*x0 + 2*x3 <= 77",
    "5*x2 + 2*x3 <= 61",
    "4*x0 + 2*x1 + 5*x2 + 2*x3 <= 61",
    "4*x0 + 1*x3 <= 24",
    "4*x2 + 1*x3 <= 19",
    "4*x0 + 5*x1 + 4*x2 + 1*x3 <= 19",
    "x0, x1, x2, x3 are integers" 
  ]
}
```

```python
import gurobipy as gp

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

    # Create variables
    x = m.addVars(4, vtype=gp.GRB.INTEGER, name=["recon", "airborne", "signal", "transport"])

    # Set objective function
    m.setObjective(5*x[0] + 2*x[1] + 4*x[2] + 8*x[3], gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(4*x[0] + 2*x[1] + 5*x[2] + 2*x[3] <= 81, "deployment_weight")
    m.addConstr(4*x[0] + 5*x[1] + 4*x[2] + 1*x[3] <= 41, "logistical_capacity")
    m.addConstr(4*x[0] + 2*x[1] >= 11)
    m.addConstr(4*x[0] + 2*x[3] >= 7)
    m.addConstr(2*x[1] + 2*x[3] >= 18)
    m.addConstr(2*x[1] + 5*x[2] >= 7)
    m.addConstr(4*x[0] + 1*x[3] >= 10)
    m.addConstr(5*x[1] + 4*x[2] >= 7)
    m.addConstr(4*x[0] + 5*x[1] + 4*x[2] >= 7) # Corrected and simplified redundant constraints
    m.addConstr(4*x[0] + 4*x[2] + 1*x[3] >= 7) # Corrected and simplified redundant constraints
    m.addConstr(5*x[1] + 4*x[2] + 1*x[3] >= 7) # Corrected and simplified redundant constraints
    m.addConstr(4*x[0] + 5*x[1] + 1*x[3] >= 7) # Corrected and simplified redundant constraints
    m.addConstr(2*x[1] + 2*x[3] <= 27)
    m.addConstr(4*x[0] + 5*x[2] <= 74)
    m.addConstr(4*x[0] + 2*x[3] <= 77)
    m.addConstr(5*x[2] + 2*x[3] <= 61)
    m.addConstr(4*x[0] + 2*x[1] + 5*x[2] + 2*x[3] <= 61)
    m.addConstr(4*x[0] + 1*x[3] <= 24)
    m.addConstr(4*x[2] + 1*x[3] <= 19)
    m.addConstr(4*x[0] + 5*x[1] + 4*x[2] + 1*x[3] <= 19)


    # Optimize model
    m.optimize()

    if m.status == gp.GRB.OPTIMAL:
        for v in m.getVars():
            print('%s %g' % (v.varName, v.x))
        print('Obj: %g' % 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')
```