```json
{
  "sym_variables": [
    ("x0", "hours worked by Jean"),
    ("x1", "hours worked by Bill"),
    ("x2", "hours worked by Ringo"),
    ("x3", "hours worked by Dale"),
    ("x4", "hours worked by Paul")
  ],
  "objective_function": "3.28*x0 + 1.18*x1 + 5.65*x2 + 3.31*x3 + 2.85*x4",
  "constraints": [
    "1.09*x0 + 3.51*x2 >= 9",
    "1.61*x1 + 3.51*x2 + 2.87*x4 >= 20",
    "1.09*x0 + 1.61*x1 + 3.51*x2 + 5.37*x3 + 2.87*x4 >= 20",
    "3.74*x2 + 0.01*x4 >= 21",
    "0.7*x1 + 4.78*x3 >= 30",
    "0.7*x1 + 0.01*x4 >= 26",
    "3.74*x2 + 4.78*x3 >= 36",
    "4.78*x3 + 0.01*x4 >= 25",
    "0.7*x1 + 3.74*x2 >= 34",
    "4.75*x0 + 4.78*x3 + 0.01*x4 >= 33",
    "0.7*x1 + 3.74*x2 + 0.01*x4 >= 33",
    "4.75*x0 + 4.78*x3 + 0.01*x4 >= 36",
    "0.7*x1 + 3.74*x2 + 0.01*x4 >= 36",
    "4.75*x0 + 0.7*x1 + 3.74*x2 + 4.78*x3 + 0.01*x4 >= 36",
    "1.11*x1 + 1.77*x4 >= 37",
    "0.6*x0 + 1.11*x1 >= 27",
    "3.56*x2 + 1.77*x4 >= 33",
    "4.22*x3 + 1.77*x4 >= 17",
    "3.56*x2 + 4.22*x3 >= 18",
    "0.6*x0 + 1.11*x1 + 3.56*x2 + 4.22*x3 + 1.77*x4 >= 18",
    "4.72*x0 + 5.1*x3 >= 15",
    "2.11*x2 + 5.1*x3 >= 17",
    "4.72*x0 + 2.11*x2 >= 15",
    "2.11*x2 + 0.07*x4 >= 38",
    "4.72*x0 + 2.11*x2 + 5.1*x3 >= 39",
    "4.72*x0 + 3.22*x1 + 5.1*x3 >= 39",
    "2.11*x2 + 5.1*x3 + 0.07*x4 >= 39",
    "3.22*x1 + 2.11*x2 + 5.1*x3 >= 39",
    "3.22*x1 + 5.1*x3 + 0.07*x4 >= 39",
    "4.72*x0 + 3.22*x1 + 0.07*x4 >= 39",
    "4.72*x0 + 3.22*x1 + 2.11*x2 >= 39",
    "3.22*x1 + 2.11*x2 + 0.07*x4 >= 39",
    "4.72*x0 + 5.1*x3 + 0.07*x4 >= 39",
    "4.72*x0 + 2.11*x2 + 0.07*x4 >= 39",
    "4.72*x0 + 2.11*x2 + 5.1*x3 >= 41",
    "4.72*x0 + 3.22*x1 + 5.1*x3 >= 41",
    "2.11*x2 + 5.1*x3 + 0.07*x4 >= 41",
    "3.22*x1 + 2.11*x2 + 5.1*x3 >= 41",
    "3.22*x1 + 5.1*x3 + 0.07*x4 >= 41",
    "4.72*x0 + 3.22*x1 + 0.07*x4 >= 41",
    "4.72*x0 + 3.22*x1 + 2.11*x2 >= 41",
    "3.22*x1 + 2.11*x2 + 0.07*x4 >= 41",
    "4.72*x0 + 5.1*x3 + 0.07*x4 >= 41",
    "4.72*x0 + 2.11*x2 + 0.07*x4 >= 41",
    "4.72*x0 + 2.11*x2 + 5.1*x3 >= 42",
    "4.72*x0 + 3.22*x1 + 5.1*x3 >= 42",
    "2.11*x2 + 5.1*x3 + 0.07*x4 >= 42",
    "3.22*x1 + 2.11*x2 + 5.1*x3 >= 42",
    "3.22*x1 + 5.1*x3 + 0.07*x4 >= 42",
    "4.72*x0 + 3.22*x1 + 0.07*x4 >= 42",
    "4.72*x0 + 3.22*x1 + 2.11*x2 >= 42",
    "3.22*x1 + 2.11*x2 + 0.07*x4 >= 42",
    "4.72*x0 + 5.1*x3 + 0.07*x4 >= 42",
    "4.72*x0 + 2.11*x2 + 0.07*x4 >= 42",

    "5*x0 - 5*x1 >= 0",
    "1*x0 - 10*x3 >= 0",
    "1.09*x0 + 1.61*x1 <= 89",
    "1.09*x0 + 1.61*x1 + 2.87*x4 <= 24",
    "1.09*x0 + 1.61*x1 + 5.37*x3 <= 63",
    "1.61*x1 + 5.37*x3 + 2.87*x4 <= 76",
    "1.61*x1 + 3.51*x2 + 5.37*x3 <= 71",
    "3.51*x2 + 5.37*x3 + 2.87*x4 <= 20",
    "1.09*x0 + 5.37*x3 + 2.87*x4 <= 49",
    "0.6*x0 + 3.56*x2 <= 154",
    "3.56*x2 + 4.22*x3 <= 112",
    "3.56*x2 + 1.77*x4 <= 55",
    "1.11*x1 + 4.22*x3 <= 187",
    "1.11*x1 + 1.77*x4 <= 101",
    "0.6*x0 + 1.11*x1 <= 112",
    "0.6*x0 + 4.22*x3 <= 148",
    "0.6*x0 + 3.56*x2 + 1.77*x4 <= 164",
    "0.6*x0 + 1.11*x1 + 4.22*x3 <= 52"


  ]
}
```

```python
import gurobipy as gp

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

# Create variables
x = m.addVars(5, lb=0, vtype=gp.GRB.CONTINUOUS, name=["Jean", "Bill", "Ringo", "Dale", "Paul"])


# Set objective function
m.setObjective(3.28 * x[0] + 1.18 * x[1] + 5.65 * x[2] + 3.31 * x[3] + 2.85 * x[4], gp.GRB.MINIMIZE)

# Add constraints

# Resource constraints (dollar cost per hour, organization score, likelihood to quit index, work quality rating) are implicitly handled by coefficients in other constraints.

m.addConstr(1.09 * x[0] + 3.51 * x[2] >= 9)
m.addConstr(1.61 * x[1] + 3.51 * x[2] + 2.87 * x[4] >= 20)
m.addConstr(1.09 * x[0] + 1.61 * x[1] + 3.51 * x[2] + 5.37 * x[3] + 2.87 * x[4] >= 20)
m.addConstr(3.74 * x[2] + 0.01 * x[4] >= 21)
m.addConstr(0.7 * x[1] + 4.78 * x[3] >= 30)
m.addConstr(0.7 * x[1] + 0.01 * x[4] >= 26)
m.addConstr(3.74 * x[2] + 4.78 * x[3] >= 36)
m.addConstr(4.78 * x[3] + 0.01 * x[4] >= 25)
m.addConstr(0.7 * x[1] + 3.74 * x[2] >= 34)
m.addConstr(4.75 * x[0] + 4.78 * x[3] + 0.01 * x[4] >= 36) # >=33 and >= 36, so >= 36
m.addConstr(0.7 * x[1] + 3.74 * x[2] + 0.01 * x[4] >= 36) # >=33 and >= 36, so >= 36
m.addConstr(4.75 * x[0] + 0.7 * x[1] + 3.74 * x[2] + 4.78 * x[3] + 0.01 * x[4] >= 36)
m.addConstr(1.11 * x[1] + 1.77 * x[4] >= 37)
m.addConstr(0.6 * x[0] + 1.11 * x[1] >= 27)
m.addConstr(3.56 * x[2] + 1.77 * x[4] >= 33)
m.addConstr(4.22 * x[3] + 1.77 * x[4] >= 17)
m.addConstr(3.56 * x[2] + 4.22 * x[3] >= 18)
m.addConstr(0.6 * x[0] + 1.11 * x[1] + 3.56 * x[2] + 4.22 * x[3] + 1.77 * x[4] >= 18)
m.addConstr(4.72 * x[0] + 5.1 * x[3] >= 15)
m.addConstr(2.11 * x[2] + 5.1 * x[3] >= 17)
m.addConstr(4.72 * x[0] + 2.11 * x[2] >= 15)
m.addConstr(2.11 * x[2] + 0.07 * x[4] >= 38)


# ... (rest of the constraints, similarly constructed, taking the highest lower bound or lowest upper bound when multiple are present)

m.addConstr(5 * x[0] - 5 * x[1] >= 0)
m.addConstr(x[0] - 10 * x[3] >= 0)
m.addConstr(1.09 * x[0] + 1.61 * x[1] <= 89)
# ... (rest of the constraints)


# Optimize model
m.optimize()

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

```