```json
{
  "sym_variables": [
    ("x0", "vincas"),
    ("x1", "carnations"),
    ("x2", "orange trees"),
    ("x3", "bean stalks")
  ],
  "objective_function": "3.53 * x0 + 8.78 * x1 + 7.88 * x2 + 1.34 * x3",
  "constraints": [
    "21 * x0 + 20 * x2 >= 29",
    "21 * x0 + 17 * x1 >= 42",
    "17 * x1 + 25 * x3 >= 33",
    "17 * x1 + 20 * x2 + 25 * x3 >= 41",
    "21 * x0 + 17 * x1 + 25 * x3 >= 41",
    "17 * x1 + 20 * x2 + 25 * x3 >= 68",
    "21 * x0 + 17 * x1 + 25 * x3 >= 68",
    "x1 + 23 * x3 >= 78",
    "5 * x0 + 23 * x3 >= 54",
    "5 * x0 + x1 >= 54",
    "x1 + 26 * x2 >= 56",
    "5 * x0 + x1 + 23 * x3 >= 70",
    "x1 + 26 * x2 + 23 * x3 >= 70",
    "5 * x0 + x1 + 26 * x2 >= 70",
    "5 * x0 + x1 + 23 * x3 >= 82",
    "x1 + 26 * x2 + 23 * x3 >= 82",
    "5 * x0 + x1 + 26 * x2 >= 82",
    "5 * x0 + x1 + 23 * x3 >= 59",
    "x1 + 26 * x2 + 23 * x3 >= 59",
    "5 * x0 + x1 + 26 * x2 >= 59",
    "-5 * x0 + 6 * x1 >= 0",
    "21 * x0 + 20 * x2 <= 172",
    "21 * x0 + 17 * x1 <= 133",
    "21 * x0 + 17 * x1 + 20 * x2 + 25 * x3 <= 133",
    "5 * x0 + 26 * x2 <= 288",
    "x1 + 23 * x3 <= 205",
    "x1 + 26 * x2 <= 169",
    "26 * x2 + 23 * x3 <= 227",
    "x1 + 26 * x2 + 23 * x3 <= 218",
    "5 * x0 + x1 + 26 * x2 <= 364",
    "5 * x0 + x1 + 26 * x2 + 23 * x3 <= 364",
    "21 * x0 + 17 * x1 + 20 * x2 + 25 * x3 <= 313",  // Resource constraint: dollar cost
    "5 * x0 + x1 + 26 * x2 + 23 * x3 <= 461"   // Resource constraint: water need
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
vincas = m.addVar(vtype=gp.GRB.INTEGER, name="vincas")
carnations = m.addVar(vtype=gp.GRB.INTEGER, name="carnations")
orange_trees = m.addVar(vtype=gp.GRB.INTEGER, name="orange_trees")
bean_stalks = m.addVar(vtype=gp.GRB.INTEGER, name="bean_stalks")

# Set objective function
m.setObjective(3.53 * vincas + 8.78 * carnations + 7.88 * orange_trees + 1.34 * bean_stalks, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(21 * vincas + 20 * orange_trees >= 29)
m.addConstr(21 * vincas + 17 * carnations >= 42)
m.addConstr(17 * carnations + 25 * bean_stalks >= 33)
m.addConstr(17 * carnations + 20 * orange_trees + 25 * bean_stalks >= 41)
m.addConstr(21 * vincas + 17 * carnations + 25 * bean_stalks >= 41)
m.addConstr(17 * carnations + 20 * orange_trees + 25 * bean_stalks >= 68)
m.addConstr(21 * vincas + 17 * carnations + 25 * bean_stalks >= 68)
m.addConstr(carnations + 23 * bean_stalks >= 78)
m.addConstr(5 * vincas + 23 * bean_stalks >= 54)
m.addConstr(5 * vincas + carnations >= 54)
m.addConstr(carnations + 26 * orange_trees >= 56)
m.addConstr(5 * vincas + carnations + 23 * bean_stalks >= 70)
m.addConstr(carnations + 26 * orange_trees + 23 * bean_stalks >= 70)
m.addConstr(5 * vincas + carnations + 26 * orange_trees >= 70)
m.addConstr(5 * vincas + carnations + 23 * bean_stalks >= 82)
m.addConstr(carnations + 26 * orange_trees + 23 * bean_stalks >= 82)
m.addConstr(5 * vincas + carnations + 26 * orange_trees >= 82)
m.addConstr(5 * vincas + carnations + 23 * bean_stalks >= 59)
m.addConstr(carnations + 26 * orange_trees + 23 * bean_stalks >= 59)
m.addConstr(5 * vincas + carnations + 26 * orange_trees >= 59)
m.addConstr(-5 * vincas + 6 * carnations >= 0)
m.addConstr(21 * vincas + 20 * orange_trees <= 172)
m.addConstr(21 * vincas + 17 * carnations <= 133)
m.addConstr(21 * vincas + 17 * carnations + 20 * orange_trees + 25 * bean_stalks <= 133)
m.addConstr(5 * vincas + 26 * orange_trees <= 288)
m.addConstr(carnations + 23 * bean_stalks <= 205)
m.addConstr(carnations + 26 * orange_trees <= 169)
m.addConstr(26 * orange_trees + 23 * bean_stalks <= 227)
m.addConstr(carnations + 26 * orange_trees + 23 * bean_stalks <= 218)
m.addConstr(5 * vincas + carnations + 26 * orange_trees <= 364)
m.addConstr(5 * vincas + carnations + 26 * orange_trees + 23 * bean_stalks <= 364)

# Resource constraints
m.addConstr(21 * vincas + 17 * carnations + 20 * orange_trees + 25 * bean_stalks <= 313)
m.addConstr(5 * vincas + carnations + 26 * orange_trees + 23 * bean_stalks <= 461)


# 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("The model is infeasible.")
else:
    print("Optimization ended with status %d" % m.status)

```
