```json
{
  "sym_variables": [
    ("x0", "hours worked by Bobby"),
    ("x1", "hours worked by Peggy"),
    ("x2", "hours worked by John"),
    ("x3", "hours worked by George"),
    ("x4", "hours worked by Dale"),
    ("x5", "hours worked by Ringo")
  ],
  "objective_function": "6*x0 + 4*x1 + 1*x2 + 8*x3 + 7*x4 + 8*x5",
  "constraints": [
    "19.71*x0 + 8.86*x5 >= 33",
    "8.35*x2 + 3.51*x4 >= 19",
    "0.29*x1 + 3.51*x4 >= 40",
    "0.29*x1 + 8.86*x5 >= 22",
    "19.71*x0 + 3.51*x4 >= 30",
    "8.35*x2 + 8.86*x5 >= 30",
    "19.71*x0 + 3.51*x4 + 8.86*x5 >= 32",
    "0.29*x1 + 8.35*x2 + 3.51*x4 >= 32",
    "8.35*x2 + 3.51*x4 + 8.86*x5 >= 32",
    "8.35*x2 + 13.53*x3 + 8.86*x5 >= 32",
    "19.71*x0 + 8.35*x2 + 13.53*x3 >= 32",
    "13.53*x3 + 3.51*x4 + 8.86*x5 >= 32",
    "8.35*x2 + 13.53*x3 + 3.51*x4 >= 32",
    "19.71*x0 + 0.29*x1 + 8.86*x5 >= 32",
    "19.71*x0 + 13.53*x3 + 8.86*x5 >= 32",
    "0.29*x1 + 3.51*x4 + 8.86*x5 >= 32",
    "0.29*x1 + 8.35*x2 + 8.86*x5 >= 32",
    "19.71*x0 + 0.29*x1 + 8.35*x2 >= 32",
    "19.71*x0 + 8.35*x2 + 3.51*x4 >= 32",
    "8.35*x2 + 3.51*x4 <= 198",
    "19.71*x0 + 8.86*x5 <= 211",
    "19.71*x0 + 8.35*x2 + 8.86*x5 <= 152",
    "8.35*x2 + 13.53*x3 + 3.51*x4 <= 112",
    "13.53*x3 + 3.51*x4 + 8.86*x5 <= 228",
    "19.71*x0 + 0.29*x1 + 8.86*x5 <= 217",
    "19.71*x0 + 3.51*x4 + 8.86*x5 <= 53"

  ]
}
```

```python
import gurobipy as gp

# Create a new model
model = gp.Model("minimize_likelihood_to_quit")

# Create variables
bobby = model.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="bobby")
peggy = model.addVar(lb=0, vtype=gp.GRB.INTEGER, name="peggy")
john = model.addVar(lb=0, vtype=gp.GRB.INTEGER, name="john")
george = model.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="george")
dale = model.addVar(lb=0, vtype=gp.GRB.INTEGER, name="dale")
ringo = model.addVar(lb=0, vtype=gp.GRB.INTEGER, name="ringo")

# Set objective function
model.setObjective(6*bobby + 4*peggy + 1*john + 8*george + 7*dale + 8*ringo, gp.GRB.MINIMIZE)

# Add constraints
model.addConstr(19.71*bobby + 8.86*ringo >= 33)
model.addConstr(8.35*john + 3.51*dale >= 19)
model.addConstr(0.29*peggy + 3.51*dale >= 40)
model.addConstr(0.29*peggy + 8.86*ringo >= 22)
model.addConstr(19.71*bobby + 3.51*dale >= 30)
model.addConstr(8.35*john + 8.86*ringo >= 30)
model.addConstr(19.71*bobby + 3.51*dale + 8.86*ringo >= 32)
model.addConstr(0.29*peggy + 8.35*john + 3.51*dale >= 32)
model.addConstr(8.35*john + 3.51*dale + 8.86*ringo >= 32)
model.addConstr(8.35*john + 13.53*george + 8.86*ringo >= 32)
model.addConstr(19.71*bobby + 8.35*john + 13.53*george >= 32)
model.addConstr(13.53*george + 3.51*dale + 8.86*ringo >= 32)
model.addConstr(8.35*john + 13.53*george + 3.51*dale >= 32)
model.addConstr(19.71*bobby + 0.29*peggy + 8.86*ringo >= 32)
model.addConstr(19.71*bobby + 13.53*george + 8.86*ringo >= 32)
model.addConstr(0.29*peggy + 3.51*dale + 8.86*ringo >= 32)
model.addConstr(0.29*peggy + 8.35*john + 8.86*ringo >= 32)
model.addConstr(19.71*bobby + 0.29*peggy + 8.35*john >= 32)
model.addConstr(19.71*bobby + 8.35*john + 3.51*dale >= 32)


model.addConstr(8.35 * john + 3.51 * dale <= 198)
model.addConstr(19.71 * bobby + 8.86 * ringo <= 211)
model.addConstr(19.71 * bobby + 8.35 * john + 8.86 * ringo <= 152)
model.addConstr(8.35 * john + 13.53 * george + 3.51 * dale <= 112)
model.addConstr(13.53 * george + 3.51 * dale + 8.86 * ringo <= 228)
model.addConstr(19.71 * bobby + 0.29 * peggy + 8.86 * ringo <= 217)
model.addConstr(19.71 * bobby + 3.51 * dale + 8.86 * ringo <= 53)



# Optimize model
model.optimize()

# Print results
if model.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % model.objVal)
    for v in model.getVars():
        print('%s %g' % (v.varName, v.x))
elif model.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {model.status}")
```