```json
{
  "sym_variables": [
    ("x0", "hours worked by Jean"),
    ("x1", "hours worked by George"),
    ("x2", "hours worked by Dale")
  ],
  "objective_function": "6.28*x0**2 + 4.64*x2**2 + 2.73*x0",
  "constraints": [
    "x1**2 + x2**2 >= 42",
    "7*x0 + 1*x1 + 17*x2 >= 42",
    "16*x0 + 10*x1 >= 49",
    "16*x0 + 10*x1 + 5*x2 >= 49",
    "-4*x0 + 10*x1 >= 0",
    "16*x0 + 5*x2 <= 125",
    "10*x1 + 5*x2 <= 143"
  ]
}
```

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

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

# Create variables
jean = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="jean")
george = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="george")
dale = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="dale")


# Set objective function
m.setObjective(6.28*jean**2 + 4.64*dale**2 + 2.73*jean, GRB.MINIMIZE)

# Add constraints
m.addConstr(george**2 + dale**2 >= 42, "c0")
m.addConstr(7*jean + 1*george + 17*dale >= 42, "c1")
m.addConstr(16*jean + 10*george >= 49, "c2")
m.addConstr(16*jean + 10*george + 5*dale >= 49, "c3")
m.addConstr(-4*jean + 10*george >= 0, "c4")
m.addConstr(16*jean + 5*dale <= 125, "c5")
m.addConstr(10*george + 5*dale <= 143, "c6")



# Optimize model
m.optimize()

if m.status == GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    print('Jean:', jean.x)
    print('George:', george.x)
    print('Dale:', dale.x)
elif m.status == GRB.INFEASIBLE:
    print('The model is infeasible.')
else:
    print('Optimization ended with status %d' % m.status)

```